
# Data
import numpy as np
import pandas as pd

# Vizualisation
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import seaborn as sns 

# Stats
import scipy.stats as stats

# Metrics
from sklearn.metrics import accuracy_score, balanced_accuracy_score, confusion_matrix, classification_report, make_scorer, roc_curve, auc

# Model selection and preprocessing
from sklearn.model_selection import train_test_split, GridSearchCV

# Machine Learning models
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from imblearn.over_sampling import SMOTE

# Utils
from tqdm.notebook import tqdm
import pickle

# Set a style to our plots
plt.style.use('ggplot')


df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00373/drug_consumption.data',header = None)
# In the case, the URL may be not valid use this code:
# df = pd.read_csv(r'data/drug_consumption.data',header = None)


column_names = ['ID','Age','Gender','Education','Country','Ethnicity',
                'Nscore','Escore','Oscore','Ascore','Cscore','Impulsive',
                'SS','Alcohol','Amphet','Amyl','Benzos','Caff','Cannabis',
                'Choc','Coke','Crack','Esctasy','Heroin','Ketamine','Legalh',
                'LSD','Meth','Mushrooms','Nicotine','Semer','VSA']
feature_names = ["Age","Gender","Education",'Country','Ethnicity',
                'Nscore','Escore','Oscore','Ascore','Cscore','Impulsive',
                'SS']
drug_names = ['Alcohol', 'Amphet','Amyl','Benzos','Caff','Cannabis','Choc','Coke','Crack','Esctasy','Heroin','Ketamine','Legalh','LSD','Meth','Mushrooms','Nicotine','Semer','VSA']
df.columns = column_names


df.head()


class_drugs = ["Never Used", "Used over a decade ago", "Used in Last Decade", "Used in the Last Year", "Used in the Last month", "Used in the Last Week", "Used in Last Day"]
class_age = ["18-24","25-34","35-44","45-54","55-64","64+"]
class_gender = ["Man", "Women"]
class_education = ["Left school before 16 years", "Left school at 16 years", "Left school at 17 years", "Left school at 18 years", "Some college or university, no certificate or degree",
                   "Professional certificate/ diploma", "University degree", "Master's degree", "Doctoral degree"]
class_country = ["USA", "New Zealand", "Other", "Australia", "Republic of Ireland", "Canada", "UK"]
class_Ethnicity = ["Black", "Asian", "White", "Mixed-White/Black", "Other", "Mixed-White/Asian", "Mixed-Black/Asian"]
class_nscore = np.arange(12,61)
class_escore = np.delete(np.arange(16,60), [1,-3])
class_oscore = np.delete(np.arange(24,61), [1,3])
class_ascore = np.delete(np.arange(12,61), [1,2,3,5,7,8,9,10])
class_cscore = np.delete(np.arange(17,60), [1,-2])


df_viz = df.copy()
df_viz.iloc[:,13:] = df.iloc[:,13:].replace(sorted(df.Cannabis.unique()),class_drugs)
df_viz.loc[:,"Age"] = df.loc[:,"Age"].replace(sorted(df.Age.unique()),class_age)
df_viz.loc[:,"Gender"] = df.loc[:,"Gender"].replace(sorted(df.Gender.unique()),class_gender)
df_viz.loc[:,"Education"] = df.loc[:,"Education"].replace(sorted(df.Education.unique()),class_education)
df_viz.loc[:,"Country"] = df.loc[:,"Country"].replace(sorted(df.Country.unique()),class_country)
df_viz.loc[:,"Ethnicity"] = df.loc[:,"Ethnicity"].replace(sorted(df.Ethnicity.unique()),class_Ethnicity)

df_viz.loc[:,"Nscore"] = df.loc[:,"Nscore"].replace(sorted(df.Nscore.unique()),class_nscore)
df_viz.loc[:,"Escore"] = df.loc[:,"Escore"].replace(sorted(df.Escore.unique()),class_escore)
df_viz.loc[:,"Oscore"] = df.loc[:,"Oscore"].replace(sorted(df.Oscore.unique()),class_oscore)
df_viz.loc[:,"Ascore"] = df.loc[:,"Ascore"].replace(sorted(df.Ascore.unique()),class_ascore)
df_viz.loc[:,"Cscore"] = df.loc[:,"Cscore"].replace(sorted(df.Cscore.unique()),class_cscore)

# We also grouped the different classes for each drugs in the way that we have a binary target: ever used or not the drug
for drug in drug_names:
    df_viz[f"{drug}_used"] = df_viz[drug].replace(class_drugs, ["Never Used","Never Used","Used","Used","Used","Used","Used"])


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1885 entries, 0 to 1884
Data columns (total 32 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   ID         1885 non-null   int64  
 1   Age        1885 non-null   float64
 2   Gender     1885 non-null   float64
 3   Education  1885 non-null   float64
 4   Country    1885 non-null   float64
 5   Ethnicity  1885 non-null   float64
 6   Nscore     1885 non-null   float64
 7   Escore     1885 non-null   float64
 8   Oscore     1885 non-null   float64
 9   Ascore     1885 non-null   float64
 10  Cscore     1885 non-null   float64
 11  Impulsive  1885 non-null   float64
 12  SS         1885 non-null   float64
 13  Alcohol    1885 non-null   object 
 14  Amphet     1885 non-null   object 
 15  Amyl       1885 non-null   object 
 16  Benzos     1885 non-null   object 
 17  Caff       1885 non-null   object 
 18  Cannabis   1885 non-null   object 
 19  Choc       1885 non-null   object 
 20  Coke       1885 non-null   object 
 21  Crack      1885 non-null   object 
 22  Esctasy    1885 non-null   object 
 23  Heroin     1885 non-null   object 
 24  Ketamine   1885 non-null   object 
 25  Legalh     1885 non-null   object 
 26  LSD        1885 non-null   object 
 27  Meth       1885 non-null   object 
 28  Mushrooms  1885 non-null   object 
 29  Nicotine   1885 non-null   object 
 30  Semer      1885 non-null   object 
 31  VSA        1885 non-null   object 
dtypes: float64(12), int64(1), object(19)
memory usage: 471.4+ KB


df.describe()


df.ID.nunique()

1885


df.Age.value_counts(normalize=True)

-0.95197    0.341114
-0.07854    0.255172
 0.49788    0.188859
 1.09449    0.155968
 1.82213    0.049337
 2.59171    0.009549
Name: Age, dtype: float64


age_density = pd.DataFrame(df_viz.Age.value_counts(normalize=True)).reset_index()

ylabels = age_density.Age.values

fig, ax = plt.subplots()
sns.barplot(x = "index", y = "Age", data = age_density, ax=ax, order=class_age)

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=y-0.35, 
            y=m+0.005, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)

ax.set_xlabel("Age")
ax.set_ylabel("")
ax.set_title("Repartition of Age")
fig.show()


plt.figure(figsize=(12,5))
df_bar = pd.DataFrame(df_viz.groupby(["Age"])["Gender"].value_counts(normalize = True))
df_bar.rename(columns = {"Gender":"Distribution"}, inplace=True)
df_bar.reset_index(inplace = True)
sns.barplot(x = "Distribution", y = "Age", hue = "Gender", order = class_age, data = df_bar)
plt.title(f"Distribution of Age depending on the gender")
plt.show()


df.Gender.value_counts(normalize=True)

-0.48246    0.500265
 0.48246    0.499735
Name: Gender, dtype: float64


gender_density = pd.DataFrame(df_viz.Gender.value_counts(normalize=True)).reset_index()
fig, ax = plt.subplots()
sns.barplot(x = "index", y = "Gender", data = gender_density, ax=ax)
ax.set_xlabel("Gender")
ax.set_ylabel("")
ax.set_title("Repartition of Gender")
fig.show()


df.Education.value_counts(normalize=True)

-0.61113    0.268435
 0.45468    0.254642
 1.16365    0.150133
-0.05921    0.143236
-1.22751    0.053050
-1.73790    0.052520
 1.98437    0.047215
-1.43719    0.015915
-2.43591    0.014854
Name: Education, dtype: float64


education_density = pd.DataFrame(df_viz.Education.value_counts(normalize=True)).reset_index()

fig, ax = plt.subplots(figsize=(16,8))
bar = sns.barplot(x = "Education", y = "index", data = education_density, ax=ax, order=class_education)

ylabels = [ax.patches[i].get_bbox().get_points()[1][0] for i in range(education_density.shape[0])]

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=m, 
            y=y, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)
    
ax.set_xlabel("")
ax.set_ylabel("Education")
ax.set_title("Repartition of Education")
fig.show()


plt.figure(figsize=(16,8))
df_bar = pd.DataFrame(df_viz.groupby(["Education"])["Gender"].value_counts(normalize = True))
df_bar.rename(columns = {"Gender":"Distribution"}, inplace=True)
df_bar.reset_index(inplace = True)
sns.barplot(x = "Distribution", y = "Education", hue = "Gender", order = class_education, data = df_bar)
plt.title(f"Distribution of Education depending on the gender")
plt.show()


df.Country.value_counts(normalize=True)

 0.96082    0.553846
-0.57009    0.295491
-0.28519    0.062599
 0.24923    0.046154
-0.09765    0.028647
 0.21128    0.010610
-0.46841    0.002653
Name: Country, dtype: float64


country_density = pd.DataFrame(df_viz.Country.value_counts(normalize=True)).reset_index()

fig, ax = plt.subplots(figsize=(16,5))
sns.barplot(x = "Country", y = "index", data = country_density, ax=ax, order=class_country)

ylabels = [ax.patches[i].get_bbox().get_points()[1][0] for i in range(country_density.shape[0])]

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=m, 
            y=y, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)
    
ax.set_xlabel("")
ax.set_ylabel("Country")
ax.set_title("Repartition of Country")
fig.show()


plt.figure(figsize=(12,5))
df_bar = pd.DataFrame(df_viz.groupby(["Country"])["Gender"].value_counts(normalize = True))
df_bar.rename(columns = {"Gender":"Distribution"}, inplace=True)
df_bar.reset_index(inplace = True)
sns.barplot(x = "Distribution", y = "Country", hue = "Gender", order = class_country, data = df_bar)
plt.title(f"Distribution of Country depending on the gender")
plt.show()


df.Ethnicity.value_counts(normalize=True)

-0.31685    0.912467
 0.11440    0.033422
-1.10702    0.017507
-0.50212    0.013793
-0.22166    0.010610
 0.12600    0.010610
 1.90725    0.001592
Name: Ethnicity, dtype: float64


ethnicity_density = pd.DataFrame(df_viz.Ethnicity.value_counts(normalize=True)).reset_index()

fig, ax = plt.subplots(figsize=(16,5))
sns.barplot(x = "Ethnicity", y = "index", data = ethnicity_density, ax=ax, order=class_Ethnicity)

ylabels = [ax.patches[i].get_bbox().get_points()[1][0] for i in range(ethnicity_density.shape[0])]

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=m, 
            y=y, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)
    
ax.set_xlabel("")
ax.set_xscale('log')

ax.set_ylabel("Ethnicity")
ax.set_title("Repartition of Ethnicity (log scale for better viz)")
fig.show()


plt.figure(figsize=(12,5))
df_bar = pd.DataFrame(df_viz.groupby(["Ethnicity"])["Gender"].value_counts(normalize = True))
df_bar.rename(columns = {"Gender":"Distribution"}, inplace=True)
df_bar.reset_index(inplace = True)
sns.barplot(x = "Distribution", y = "Ethnicity", hue = "Gender", order = class_Ethnicity, data = df_bar)
plt.title(f"Distribution of Ethnicity depending on the gender")
plt.show()


plt.figure(figsize=(12,10))
df_bar = pd.DataFrame(df_viz.groupby(["Ethnicity"])["Country"].value_counts(normalize = True))
df_bar.rename(columns = {"Country":"Distribution"}, inplace=True)
df_bar.reset_index(inplace = True)
sns.barplot(x = "Distribution", y = "Ethnicity", hue = "Country", order = class_Ethnicity, data = df_bar)
plt.title(f"Distribution of Ethnicity depending on the country")
plt.show()


plt.figure(figsize=(12,10))
df_bar = pd.DataFrame(df_viz.groupby(["Ethnicity"])["Education"].value_counts(normalize = True))
df_bar.rename(columns = {"Education":"Distribution"}, inplace=True)
df_bar.reset_index(inplace = True)
sns.barplot(x = "Distribution", y = "Ethnicity", hue = "Education", order = class_Ethnicity, data = df_bar)
plt.title(f"Distribution of Ethnicity depending on the Education")
plt.show()


nrow = 5
ncol = 1
fig, axes = plt.subplots(nrow, ncol, figsize = (24,6*nrow))

for i, name in enumerate([f"{letter}score" for letter in ["N","E","O","A","C"]]):
    score_density = pd.DataFrame(df_viz[name].value_counts(normalize=True)).reset_index()
    score_density.loc[:,"index"] = score_density["index"].apply(round)

    ylabels = score_density.sort_values(by="index")[name].values

    sns.barplot(x = "index", y = name, data = score_density, ax=axes[i])

    bars = [r for r in axes[i].get_children() if type(r)==Rectangle]
    colors = [c.get_facecolor() for c in bars[:-1]] 
    for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
        axes[i].text(x=y-0.35, 
                y=m+0.0005, 
                s=str(round(m*100,1))+"%", 
                color=c,
                fontsize=11)

    axes[i].set_xlabel(name)
    axes[i].set_ylabel("")
    axes[i].set_title(f"Repartition of {name}")

fig.show()


nrow = 2
ncol = 3
fig, axes = plt.subplots(nrow, ncol, figsize = (14,8))

for i, name in enumerate([f"{letter}score" for letter in ["N","E","O","A","C"]]):
    sns.histplot(df_viz[name],kde=True, stat="density", ax=axes[i//ncol,i%ncol])
    axes[i//ncol,i%ncol].set_title(f"Density plot of {name}")

fig.tight_layout()
fig.show()


df.Impulsive.value_counts(normalize=True)

-0.21712    0.188329
-0.71126    0.162865
-1.37983    0.146419
 0.19268    0.136340
 0.52975    0.114589
 0.88113    0.103448
 1.29221    0.078515
 1.86203    0.055172
-2.55524    0.010610
 2.90161    0.003714
Name: Impulsive, dtype: float64


impulsive_density = pd.DataFrame(df_viz.Impulsive.value_counts(normalize=True)).reset_index()
impulsive_density.loc[:,"index"] = impulsive_density["index"].apply(lambda x: round(x,2))

ylabels = impulsive_density.sort_values(by="index").Impulsive.values

fig, ax = plt.subplots(figsize=(15,5))
sns.barplot(x = "index", y = "Impulsive", data = impulsive_density, ax=ax)

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=y-0.25, 
            y=m+0.0025, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)

ax.set_xlabel("Impulsive")
ax.set_ylabel("")
ax.set_title("Repartition of Impulsive")
fig.show()


SS_density = pd.DataFrame(df_viz.SS.value_counts(normalize=True)).reset_index()
SS_density.loc[:,"index"] = SS_density["index"].apply(lambda x: round(x,2))

ylabels = SS_density.sort_values(by="index").SS.values

fig, ax = plt.subplots(figsize=(15,4))
sns.barplot(x = "index", y = "SS", data = SS_density, ax=ax)

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=y-0.25, 
            y=m+0.0025, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)

ax.set_xlabel("SS")
ax.set_ylabel("")
ax.set_title("Repartition of SS")
fig.show()


nrows = 4
ncols = 5
fig, axis = plt.subplots(nrows, ncols, figsize=(35,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        drug_density = pd.DataFrame(df_viz.iloc[:,13+i].value_counts(normalize=True)).reset_index()
        sns.barplot(y = "index", x = df_viz.iloc[:,13:].columns[i], data = drug_density, ax=axis[r,c], order=class_drugs)
        ylabels = [axis[r,c].patches[i].get_bbox().get_points()[1][0] for i in range(drug_density.shape[0])]

        bars = [r for r in axis[r,c].get_children() if type(r)==Rectangle]
        colors = [c.get_facecolor() for c in bars[:-1]] 
        for y, m, color in zip(np.arange(len(ylabels)), ylabels, colors):
            axis[r,c].text(x=m, 
                    y=y, 
                    s=str(round(m*100,2))+"%", 
                    color=color,
                    fontsize=11)

        axis[r,c].set_title(f"Class of {df_viz.iloc[:,13:].columns[i]}")
        i+=1
        if i == 19:
          break
fig.tight_layout(pad=3.0)
fig.show()


nrows = 4
ncols = 5
fig, axis = plt.subplots(nrows, ncols, figsize=(30,15))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        drug_density = pd.DataFrame(df_viz.iloc[:,32+i].value_counts(normalize=True)).reset_index()
        sns.barplot(x = "index", y = df_viz.iloc[:,32:].columns[i], data = drug_density, ax=axis[r,c], order=["Never Used", "Used"])
        axis[r,c].set_title(f"Class of {df_viz.iloc[:,32:].columns[i]}")
        axis[r,c].tick_params(axis='x')
        i+=1
        if i == 19:
          break
fig.tight_layout(pad=3.0)
fig.show()


nb_drugs = df_viz.iloc[:,32:].apply(pd.Series.value_counts, axis=1).fillna(0)
df_viz["Number of drug used"] = nb_drugs.Used


nb_drugs_density = pd.DataFrame(df_viz["Number of drug used"].value_counts(normalize=True)).reset_index()

ylabels = nb_drugs_density.sort_values(by="index")["Number of drug used"].values

fig, ax = plt.subplots(figsize=(14,6))
sns.barplot(x = "index", y = "Number of drug used", data = nb_drugs_density, ax=ax)

bars = [r for r in ax.get_children() if type(r)==Rectangle]
colors = [c.get_facecolor() for c in bars[:-1]] 
for y, m, c in zip(np.arange(len(ylabels)), ylabels, colors):
    ax.text(x=y-0.35, 
            y=m+0.005, 
            s=str(round(m*100,2))+"%", 
            color=c,
            fontsize=11)

ax.set_xlabel("Number of drug used")
ax.set_ylabel("")
ax.set_title("Repartition of Number of drug used")
fig.show()


scores_drugs = df_viz.groupby("Number of drug used")[["Nscore","Escore","Oscore","Ascore","Cscore"]].mean()
scores_drugs.plot(figsize=(12,8))
plt.title("Scores depending on the number of drug used")
plt.show()


other_test_drugs = df_viz.groupby("Number of drug used")[["Impulsive","SS"]].mean()
other_test_drugs.plot(figsize=(12,8))
plt.title("Scores depending on the number of drug used")
plt.show()


all_scores_drugs = df_viz.groupby("Number of drug used")[["Nscore","Escore","Oscore","Ascore","Cscore","Impulsive","SS"]].mean()
plt.figure(figsize=(5,10))
sns.heatmap(pd.DataFrame(all_scores_drugs.reset_index().corr().iloc[1:,0]),annot=True,fmt='.2f',cmap='coolwarm',vmax=1.0,vmin=-1.0)
plt.show()


df.isna().sum()/df.shape[0]

ID           0.0
Age          0.0
Gender       0.0
Education    0.0
Country      0.0
Ethnicity    0.0
Nscore       0.0
Escore       0.0
Oscore       0.0
Ascore       0.0
Cscore       0.0
Impulsive    0.0
SS           0.0
Alcohol      0.0
Amphet       0.0
Amyl         0.0
Benzos       0.0
Caff         0.0
Cannabis     0.0
Choc         0.0
Coke         0.0
Crack        0.0
Esctasy      0.0
Heroin       0.0
Ketamine     0.0
Legalh       0.0
LSD          0.0
Meth         0.0
Mushrooms    0.0
Nicotine     0.0
Semer        0.0
VSA          0.0
dtype: float64


plt.figure(figsize=(15,10))
sns.heatmap(df.corr(),annot=True,fmt='.2f',cmap='Spectral_r',vmax=1.0,vmin=-1.0)
plt.show()


df2 = df.copy(deep=True)
df2.iloc[:,13:] = df2.iloc[:,13:].replace(['CL0','CL1','CL2','CL3','CL4','CL5','CL6'],[0,1,2,3,4,5,6])


plt.figure(figsize=(30,20))
sns.heatmap(df2.corr(),annot=True,fmt='.2f',cmap='Spectral_r',vmax=1.0,vmin=-1.0)
plt.show()


nrows = 4
ncols = 5
fig, axis = plt.subplots(nrows, ncols, figsize=(15,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        if i!=ncols*nrows-1:     
            all_scores_drugs = df2.groupby(df2.columns[i+13])[["Nscore","Escore","Oscore","Ascore","Cscore","Impulsive","SS"]].mean()
            sns.heatmap(pd.DataFrame(all_scores_drugs.reset_index().corr().iloc[1:,0]),annot=True,fmt='.2f',cmap='coolwarm',vmax=1.0,vmin=-1.0, ax=axis[r,c])
            axis[r,c].set_title(f"Class of {df2.columns[13+i]}")
            i+=1
fig.tight_layout(pad=3.0)
fig.show()


nrows = 5
ncols = 4
fig, axis = plt.subplots(nrows, ncols, figsize=(30,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        if i!=ncols*nrows-1:     
            df_bar = pd.DataFrame(df_viz.groupby(["Age"])[f"{drug_names[i]}_used"].value_counts(normalize = True))
            df_bar.rename(columns = {f"{drug_names[i]}_used":drug_names[i]}, inplace = True)
            df_bar.reset_index(inplace = True)
            sns.barplot(x = f"{drug_names[i]}_used", y = drug_names[i], hue = "Age", data = df_bar, order=["Never Used","Used"], ax=axis[r,c])
            axis[r,c].set_title(f"Class of {df.iloc[:,13:].columns[i]}")
            i+=1
fig.tight_layout(pad=3.0)
fig.show()


nrows = 5
ncols = 4
fig, axis = plt.subplots(nrows, ncols, figsize=(30,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        if i!=ncols*nrows-1:     
            df_bar = pd.DataFrame(df_viz.groupby(["Gender"])[f"{drug_names[i]}_used"].value_counts(normalize = True))
            df_bar.rename(columns = {f"{drug_names[i]}_used":drug_names[i]}, inplace = True)
            df_bar.reset_index(inplace = True)
            sns.barplot(x = f"{drug_names[i]}_used", y = drug_names[i], hue = "Gender", data = df_bar, order = ["Never Used","Used"], ax=axis[r,c])
            axis[r,c].set_title(f"Class of {df.iloc[:,13:].columns[i]}")
            i+=1
fig.tight_layout(pad=3.0)
fig.show()


nrows = 5
ncols = 4
fig, axis = plt.subplots(nrows, ncols, figsize=(30,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        if i!=ncols*nrows-1:     
            df_bar = pd.DataFrame(df_viz.groupby(["Education"])[f"{drug_names[i]}_used"].value_counts(normalize = True))
            df_bar.rename(columns = {f"{drug_names[i]}_used":drug_names[i]}, inplace = True)
            df_bar.reset_index(inplace = True)
            sns.barplot(x = f"{drug_names[i]}_used", y = drug_names[i], hue = "Education", order = ["Never Used","Used"], data = df_bar, ax=axis[r,c])
            axis[r,c].set_title(f"Class of {df.iloc[:,13:].columns[i]}")
            i+=1
fig.tight_layout(pad=3.0)
fig.show()


nrows = 5
ncols = 4
fig, axis = plt.subplots(nrows, ncols, figsize=(30,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        if i!=ncols*nrows-1:     
            df_bar = pd.DataFrame(df_viz.groupby(["Country"])[f"{drug_names[i]}_used"].value_counts(normalize = True))
            df_bar.rename(columns = {f"{drug_names[i]}_used":drug_names[i]}, inplace = True)
            df_bar.reset_index(inplace = True)
            sns.barplot(x = f"{drug_names[i]}_used", y = drug_names[i], hue = "Country", order = ["Never Used","Used"], data = df_bar, ax=axis[r,c])
            axis[r,c].set_title(f"Class of {df.iloc[:,13:].columns[i]}")
            i+=1
fig.tight_layout(pad=3.0)
fig.show()


nrows = 5
ncols = 4
fig, axis = plt.subplots(nrows, ncols, figsize=(30,20))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        if i!=ncols*nrows-1:     
            df_bar = pd.DataFrame(df_viz.groupby(["Ethnicity"])[f"{drug_names[i]}_used"].value_counts(normalize = True))
            df_bar.rename(columns = {f"{drug_names[i]}_used":drug_names[i]}, inplace = True)
            df_bar.reset_index(inplace = True)
            sns.barplot(x = f"{drug_names[i]}_used", y = drug_names[i], hue = "Ethnicity", order = ["Never Used","Used"], data = df_bar, ax=axis[r,c])
            axis[r,c].set_title(f"Class of {df.iloc[:,13:].columns[i]}")
            i+=1
fig.tight_layout(pad=3.0)
fig.show()


plt.figure(figsize=(15,10))
sns.heatmap(df2.iloc[:,13:].corr(),annot=True,fmt='.2f',cmap='Spectral_r',vmax=1.0,vmin=-1.0)
plt.show()


from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd

pca_out = PCA().fit(df.loc[:,feature_names])

num_pc = pca_out.n_features_
pc_list = ["PC"+str(i) for i in list(range(1, num_pc+1))]

plt.bar(pc_list,np.round(pca_out.explained_variance_ratio_*100))
plt.xticks(rotation=45)
plt.xlabel("Proportion of variance (%)")
plt.ylabel("PCs")
plt.show()


pca_out = PCA()
pca_x = pca_out.fit_transform(df.loc[:,feature_names])

def biplot(score,coeff,labels=None):
    xs = score[:,0]
    ys = score[:,1]
    n = coeff.shape[0]
    scalex = 1.0/(xs.max() - xs.min())
    scaley = 1.0/(ys.max() - ys.min())
    plt.figure(figsize=(10,10))
    plt.scatter(xs * scalex,ys * scaley)
    for i in range(n):
        plt.arrow(0, 0, coeff[i,0], coeff[i,1],color = 'r',alpha = 0.5)
        if labels is None:
            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, feature_names[i], color = 'g', ha = 'center', va = 'center')
        else:
            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color = 'g', ha = 'center', va = 'center')
    plt.xlim(-1,1)
    plt.ylim(-1,1)
    plt.xlabel("PC{}".format(1))
    plt.ylabel("PC{}".format(2))
    plt.grid()

biplot(pca_x[:,0:2],np.transpose(pca_out.components_[0:2, :]))
plt.show()


df = df.drop("Semer",axis=1)


df.iloc[:,13:] = df.iloc[:,13:].replace(['CL0','CL1','CL2','CL3','CL4','CL5','CL6'],[0,1,2,3,4,5,6])


#remove columns ID from df
df.drop('ID',axis=1,inplace=True)

#modify the column country to be one hot encoded
list_of_countries = ['USA','New zealand','Other_Country','Australia','Republic of ireland','Canada','UK']
df.Country = df.Country.replace(df.Country.unique(),list_of_countries)


#modify the column ethnicity to be one hot encoded
list_of_ethnicity = ['Black','Asian','White','Mix_White_Black','Other_Ethnicity','Mix_White_Asian','Mix_Asian_Black']
df.Ethnicity = df.Ethnicity.replace(df.Ethnicity.unique(),list_of_ethnicity)


df = pd.concat([df.iloc[:,:3],pd.get_dummies(df.Country),pd.get_dummies(df.Ethnicity),df.iloc[:,5:]],axis = 1)


X = df.iloc[:,:24]
y = df.iloc[:,24:]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)


y.iloc[:,].value_counts()

Alcohol  Amphet  Amyl  Benzos  Caff  Cannabis  Choc  Coke  Crack  Esctasy  Heroin  Ketamine  Legalh  LSD  Meth  Mushrooms  Nicotine  VSA
5        0       0     0       6     0         6     0     0      0        0       0         0       0    0     0          0         0      29
6        0       0     0       6     0         6     0     0      0        0       0         0       0    0     0          0         0      19
5        0       0     0       6     0         5     0     0      0        0       0         0       0    0     0          0         0      17
6        0       0     0       6     0         5     0     0      0        0       0         0       0    0     0          0         0      13
4        0       0     0       6     0         6     0     0      0        0       0         0       0    0     0          0         0      11
                                                                                                                                            ..
5        3       2     0       6     3         5     3     0      3        0       0         2       3    0     2          6         0       1
                                     4         5     3     0      4        0       3         3       1    0     1          5         1       1
                                     6         5     5     2      4        0       2         0       2    2     2          6         1       1
                                               6     0     0      3        0       0         3       3    3     2          3         0       1
0        0       0     0       0     0         0     0     0      0        0       0         0       0    0     0          0         0       1
Length: 1662, dtype: int64


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]
scores = dict()
scoring = ['accuracy', 'balanced_accuracy', 'precision']

for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        score_ = model.score(X_test, y_test_i)
        scores_models.append(score_)

    scores[y.columns[i]] = scores_models
    print(y.columns[i],': ',model_names[np.argmax(scores_models)], ": ", max(scores_models))

Alcohol :  LogisticRegression :  0.4452296819787986
Amphet :  SVC :  0.5388692579505301
Amyl :  RandomForestClassifier :  0.715547703180212
Benzos :  SVC :  0.5459363957597173
Caff :  LogisticRegression :  0.7579505300353356
Cannabis :  SVC :  0.43992932862190814
Choc :  SVC :  0.41519434628975266
Coke :  SVC :  0.5971731448763251
Crack :  SVC :  0.8798586572438163
Esctasy :  SVC :  0.5812720848056537
Heroin :  SVC :  0.8586572438162544
Ketamine :  SVC :  0.803886925795053
Legalh :  SVC :  0.6466431095406361
LSD :  RandomForestClassifier :  0.6254416961130742
Meth :  LogisticRegression :  0.7614840989399293
Mushrooms :  SVC :  0.5830388692579506
Nicotine :  RandomForestClassifier :  0.4045936395759717
VSA :  SVC :  0.784452296819788


All_scores = pd.DataFrame(scores, index=model_names)
All_scores['means_models'] = All_scores.mean(axis=1)
All_scores.loc['means_drugs'] = All_scores.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]
scores = dict()
scoring = ['accuracy', 'balanced_accuracy', 'precision']

for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test)
        score_ = balanced_accuracy_score(y_test_i, preds)
        scores_models.append(score_)

    scores[y.columns[i]] = scores_models
    print(y.columns[i],': ',model_names[np.argmax(scores_models)], ": ", max(scores_models))

Alcohol :  BernoulliNB :  0.23902397029765146
Amphet :  BernoulliNB :  0.2295992111449647

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Amyl :  BaggingClassifier :  0.22885531920419713
Benzos :  BernoulliNB :  0.1989051914721398
Caff :  DecisionTreeClassifier :  0.14870640393288476
Cannabis :  BaggingClassifier :  0.3028878517471365

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Choc :  DecisionTreeClassifier :  0.18184463951389587
Coke :  BaggingClassifier :  0.22948727659594553

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Crack :  BernoulliNB :  0.23380283494424267
Esctasy :  BernoulliNB :  0.21061562822852395
Heroin :  BernoulliNB :  0.217939027462837
Ketamine :  DecisionTreeClassifier :  0.1868110064538636
Legalh :  BernoulliNB :  0.2507777426857773
LSD :  BernoulliNB :  0.3122374515252853
Meth :  BernoulliNB :  0.20461207738909518

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Mushrooms :  SGDClassifier :  0.2949135507931328
Nicotine :  GradientBoostingClassifier :  0.22979205776743394
VSA :  BernoulliNB :  0.19564389462348647


All_scores = pd.DataFrame(scores, index=model_names)
All_scores['means_models'] = All_scores.mean(axis=1)
All_scores.loc['means_drugs'] = All_scores.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]

nrows = 3
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(16,11))
i = 0
k = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train.iloc[:,i]
        y_test_i = y_test.iloc[:,i]
        models[k].fit(X_train, y_train_i)
        preds = models[k].predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + model_names[k])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        k+=1 
fig.tight_layout(pad=3.0)
fig.show()


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000, class_weight="balanced"), 
          SVC(class_weight="balanced"), 
          KNeighborsClassifier(), 
          DecisionTreeClassifier(class_weight="balanced"), 
          RandomForestClassifier(class_weight="balanced"), 
          BaggingClassifier(), 
          GradientBoostingClassifier(), 
          SGDClassifier(class_weight="balanced"), 
          BernoulliNB()]


scores = dict()
for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test)
        score_ = balanced_accuracy_score(y_test_i, preds)
        scores_models.append(score_)

    scores[y.columns[i]] = scores_models
    print(y.columns[i],': ',model_names[np.argmax(scores_models)], ": ", max(scores_models))

Alcohol :  LogisticRegression :  0.24522677037978702
Amphet :  LogisticRegression :  0.30368846681742745

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Amyl :  SVC :  0.305454586774713
Benzos :  LogisticRegression :  0.2914673465745748
Caff :  SVC :  0.2507637542480748
Cannabis :  LogisticRegression :  0.31516812330775806

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Choc :  SVC :  0.2730739582282313
Coke :  SVC :  0.29050479980489413

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Crack :  LogisticRegression :  0.3078478187070451
Esctasy :  SVC :  0.25587805737054065
Heroin :  DecisionTreeClassifier :  0.2537473389785329
Ketamine :  SVC :  0.23622171479314338
Legalh :  SGDClassifier :  0.2539017932262738
LSD :  LogisticRegression :  0.3952919248807337
Meth :  SVC :  0.2747131410703817

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Mushrooms :  SVC :  0.37316195610755587
Nicotine :  SVC :  0.2736514972444228
VSA :  SVC :  0.253473590208284


All_scores = pd.DataFrame(scores, index=model_names)
All_scores['means_models'] = All_scores.mean(axis=1)
All_scores.loc['means_drugs'] = All_scores.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


nrows = 3
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(16,11))
i = 0
k = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train.iloc[:,i]
        y_test_i = y_test.iloc[:,i]
        models[k].fit(X_train, y_train_i)
        preds = models[k].predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + model_names[k])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        k+=1 
fig.tight_layout(pad=3.0)
fig.show()


# We group the classes with less than 6 samples (condition required by the SMOTE algorithm)
dict_1 = {0:0,1:1,2:2,3:3,4:4,5:5,6:5}
dict_2 = {0:0,1:0,2:1,3:2,4:3,5:4,6:5} 

df_classed2 = df.copy()
df_classed2.iloc[:,26] = df_classed2.iloc[:,26].replace(dict_1.keys(),dict_1.values())
df_classed2.iloc[:,30] = df_classed2.iloc[:,30].replace(dict_2.keys(),dict_2.values())
df_classed2.iloc[:,32] = df_classed2.iloc[:,32].replace(dict_1.keys(),dict_1.values())
df_classed2.iloc[:,35] = df_classed2.iloc[:,35].replace(dict_1.keys(),dict_1.values())
df_classed2.iloc[:,39] = df_classed2.iloc[:,39].replace(dict_1.keys(),dict_1.values())
df_classed2.iloc[:,41] = df_classed2.iloc[:,41].replace(dict_1.keys(),dict_1.values())


X = df_classed2.iloc[:,:24]
y = df_classed2.iloc[:,24:]
X_train_sampled, X_test_sampled, y_train_sampled, y_test_sampled = train_test_split(X, y, test_size=0.3, random_state=0)


oversample = SMOTE()
list_X = dict()
list_y = dict()
for i, name in enumerate(y_train_sampled.columns):
    try:
        new_X, new_y = oversample.fit_resample(X_train_sampled, y_train_sampled.iloc[:,i])
    except Exception as e:
        print(i,e)
    list_X[name] = new_X
    list_y[name] = new_y


scores = dict()
for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    X_train_i = list_X[y.columns[i]]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test_sampled)
        score_ = balanced_accuracy_score(y_test_i, preds)
        scores_models.append(score_)

    scores[y.columns[i]] = scores_models
    print(y.columns[i],': ',model_names[np.argmax(scores_models)], ": ", max(scores_models))

Alcohol :  SGDClassifier :  0.25865473462383987
Amphet :  LogisticRegression :  0.30368846681742745

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Amyl :  SVC :  0.305454586774713
Benzos :  LogisticRegression :  0.2914673465745748
Caff :  SVC :  0.2507637542480748
Cannabis :  LogisticRegression :  0.31516812330775806

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Choc :  SVC :  0.2730739582282313
Coke :  SVC :  0.29050479980489413

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Crack :  LogisticRegression :  0.3078478187070451
Esctasy :  SVC :  0.25587805737054065
Heroin :  DecisionTreeClassifier :  0.24014189680166217
Ketamine :  SVC :  0.23622171479314338
Legalh :  LogisticRegression :  0.25094294949334306
LSD :  SGDClassifier :  0.3991095679946888
Meth :  SVC :  0.2747131410703817

/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1953: UserWarning: y_pred contains classes not in y_true
  warnings.warn("y_pred contains classes not in y_true")

Mushrooms :  SVC :  0.37316195610755587
Nicotine :  SVC :  0.2736514972444228
VSA :  SVC :  0.253473590208284


All_scores = pd.DataFrame(scores, index=model_names)
All_scores['means_models'] = All_scores.mean(axis=1)
All_scores.loc['means_drugs'] = All_scores.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


X_train_sampled, y_train_sampled = list_X["Alcohol"], list_y["Alcohol"]
y_test_sampled = y_test_sampled.loc[:,"Alcohol"]


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]

nrows = 3
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(30,15))
k = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train_sampled
        y_test_i = y_test_sampled
        models[k].fit(X_train_sampled, y_train_i)
        preds = models[k].predict(X_test_sampled)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + model_names[k])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        k+=1 
fig.tight_layout(pad=3.0)
fig.show()


df = df.drop("Choc",axis=1) #ATTENTION CA CHANGE LES COLONNES


liste = [["Alcohol","Cannabis", "Nicotine","Amphet", "Benzos", "Coke", "Esctasy", "Legalh"],
         ["Amyl", "Crack", "Heroin", "Ketamine", "Meth", "VSA","LSD", "Mushrooms"], 
         ["Caff"]]
dict_1 = {0:0,1:0,2:0,3:0,4:1,5:1,6:1} # Alcohol, Cannabis, Nicotine, Amphet, Benzos, Coke, Ecstasy, Legalh, LSD, mushrooms
# 0: never used in the last month => not addict
# 1: used last month => addict
dict_2 = {0:0,1:0,2:1,3:1,4:1,5:1,6:1} # Amyl, Crack, Heroin, Ketamine, Meth, VSA
# 0: never used 
# 1: used 
dict_3 = {0:0,1:0,2:0,3:0,4:0,5:0,6:1} # Caff
# 0: not used yesterday => not daily addict
# 1: used yesterday => daily addict

list_of_dicts = [dict_1,dict_2,dict_3]

df_classed = df.copy()
for l, d in zip(liste, list_of_dicts):
    for i in l:
        df_classed[i] = df_classed[i].replace(d.keys(),d.values())


nrows = 3
ncols = 6
fig, axis = plt.subplots(nrows, ncols, figsize=(30,15))
i = 0
for r in range(nrows):
    for c in range(ncols):  
        drug_density = pd.DataFrame(df_classed.iloc[:,24+i].value_counts(normalize=True)).reset_index()
        sns.barplot(x = "index", y = df_classed.iloc[:,24:].columns[i], data = drug_density, ax=axis[r,c])
        axis[r,c].set_title(f"Class of {df_classed.iloc[:,24:].columns[i]}")
        axis[r,c].tick_params(axis='x')
        i+=1
        if i == 17:
          break
fig.tight_layout(pad=3.0)
fig.show()


X = df_classed.iloc[:,:24]
y = df_classed.iloc[:,24:]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)


# Alcohol
y_train_i = y_train.loc[:,"Alcohol"]
y_test_i = y_test.loc[:,"Alcohol"]


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]
scores_basic = dict()
best_model_basic = dict()
best_model_name_basic = dict()

for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test)
        score_ = balanced_accuracy_score(y_test_i, preds)
        scores_models.append(score_)

    scores_basic[y.columns[i]] = scores_models
    best_model_basic[y.columns[i]] = models[np.argmax(scores_models)]
    best_model_name_basic[y.columns[i]] = model_names[np.argmax(scores_models)]
    print(y.columns[i],': ', model_names[np.argmax(scores_models)], ": ", max(scores_models), sep="")

Alcohol: SGDClassifier: 0.5370574206567602
Amphet: BernoulliNB: 0.641136675123203
Amyl: DecisionTreeClassifier: 0.6308483183483183
Benzos: BernoulliNB: 0.696819132750095
Caff: SGDClassifier: 0.5577135759617512
Cannabis: SGDClassifier: 0.8160249405412354
Coke: BernoulliNB: 0.5278580639423718
Crack: BernoulliNB: 0.6548573007539573
Esctasy: DecisionTreeClassifier: 0.5603991596638656
Heroin: BernoulliNB: 0.6728535484093338
Ketamine: BernoulliNB: 0.5897306961671696
Legalh: BernoulliNB: 0.6539688164422395
LSD: BernoulliNB: 0.7722229227083596
Meth: BernoulliNB: 0.7552911983652022
Mushrooms: BernoulliNB: 0.762484757787385
Nicotine: BernoulliNB: 0.6793238669713886
VSA: BernoulliNB: 0.6979076940176165


All_scores_basic = pd.DataFrame(scores_basic, index=model_names)
All_scores_basic['means_models'] = All_scores_basic.mean(axis=1)
All_scores_basic.loc['means_drugs'] = All_scores_basic.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores_basic,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]

nrows = 3
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(16,11))
i = 0
k = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train.iloc[:,i]
        y_test_i = y_test.iloc[:,i]
        models[k].fit(X_train, y_train_i)
        preds = models[k].predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized,vmin=0, vmax=100, annot=True, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + model_names[k])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        k+=1 
fig.tight_layout(pad=3.0)
fig.show()


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000, class_weight="balanced"), 
          SVC(class_weight="balanced"), 
          KNeighborsClassifier(), 
          DecisionTreeClassifier(class_weight="balanced"), 
          RandomForestClassifier(class_weight="balanced"), 
          BaggingClassifier(), 
          GradientBoostingClassifier(), 
          SGDClassifier(class_weight="balanced"), 
          BernoulliNB()]


scores_weight = dict()
best_model_weight = dict()
best_model_name_weight = dict()
i=0
for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test)
        score_ = balanced_accuracy_score(y_test_i, preds)
        scores_models.append(score_)

    scores_weight[y.columns[i]] = scores_models
    best_model_weight[y.columns[i]] = models[np.argmax(scores_models)]
    best_model_name_weight[y.columns[i]] = model_names[np.argmax(scores_models)]
    print(y.columns[i],': ',model_names[np.argmax(scores_models)], ": ", max(scores_models))

Alcohol :  LogisticRegression :  0.6295633828655293
Amphet :  SGDClassifier :  0.6919050535094626
Amyl :  LogisticRegression :  0.6800907425907425
Benzos :  LogisticRegression :  0.7430581970330924
Caff :  LogisticRegression :  0.6021472444830109
Cannabis :  LogisticRegression :  0.8144500867776563
Coke :  SVC :  0.6809106674374139
Crack :  SVC :  0.6936012315951525
Esctasy :  SVC :  0.6406162464985994
Heroin :  SVC :  0.7241581360079964
Ketamine :  SVC :  0.6637613141133087
Legalh :  LogisticRegression :  0.7344377510040161
LSD :  LogisticRegression :  0.7921605093935191
Meth :  LogisticRegression :  0.7608925704276748
Mushrooms :  BernoulliNB :  0.762484757787385
Nicotine :  BernoulliNB :  0.6793238669713886
VSA :  LogisticRegression :  0.7479090833310178


All_scores_weight = pd.DataFrame(scores_weight, index=model_names)
All_scores_weight['means_models'] = All_scores_weight.mean(axis=1)
All_scores_weight.loc['means_drugs'] = All_scores_weight.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores_weight,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


nrows = 3
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(16,11))
k = 0
i = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train.iloc[:,i]
        y_test_i = y_test.iloc[:,i]
        models[k].fit(X_train, y_train_i)
        preds = models[k].predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True,vmin=0, vmax=100, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + model_names[k])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        k+=1 
fig.tight_layout(pad=3.0)
fig.show()


oversample = SMOTE()
list_X = dict()
list_y = dict()
for i, name in enumerate(y_train.columns):
    try:
        new_X, new_y = oversample.fit_resample(X_train, y_train.iloc[:,i])
    except Exception as e:
        print(i,e)
    list_X[name] = new_X
    list_y[name] = new_y


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]

scores_sampling = dict()
best_model_sampling = dict()
best_model_name_sampling = dict()
for i in tqdm(range(len(y.columns))):
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    X_train_i = list_X[y.columns[i]]
    scores_models = list()
    for model, model_name in zip(models, model_names):
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test)
        score_ = balanced_accuracy_score(y_test_i, preds)
        scores_models.append(score_)
    best_model_sampling[y.columns[i]] = models[np.argmax(scores_models)]
    best_model_name_sampling[y.columns[i]] = model_names[np.argmax(scores_models)]
    scores_sampling[y.columns[i]] = scores_models
    print(y.columns[i],': ',model_names[np.argmax(scores_models)], ": ", max(scores_models))

Alcohol :  BaggingClassifier :  0.5480875068794716
Amphet :  BernoulliNB :  0.641136675123203
Amyl :  DecisionTreeClassifier :  0.6163003663003663
Benzos :  BernoulliNB :  0.696819132750095
Caff :  DecisionTreeClassifier :  0.543633981590186
Cannabis :  BernoulliNB :  0.7977694928328084
Coke :  BernoulliNB :  0.5278580639423718
Crack :  BernoulliNB :  0.6548573007539573
Esctasy :  DecisionTreeClassifier :  0.5574579831932773
Heroin :  BernoulliNB :  0.6728535484093338
Ketamine :  BernoulliNB :  0.5897306961671696
Legalh :  BernoulliNB :  0.6539688164422395
LSD :  BernoulliNB :  0.7722229227083596
Meth :  BernoulliNB :  0.7552911983652022
Mushrooms :  BernoulliNB :  0.762484757787385
Nicotine :  RandomForestClassifier :  0.6899766463102419
VSA :  BernoulliNB :  0.6979076940176165


All_scores_sampling = pd.DataFrame(scores_sampling, index=model_names)
All_scores_sampling['means_models'] = All_scores_sampling.mean(axis=1)
All_scores_sampling.loc['means_drugs'] = All_scores_sampling.mean(axis=0)
plt.figure(figsize=(15,7))
sns.heatmap(All_scores_sampling,annot=True,cmap = 'inferno',vmax=1.0,vmin=0.0)# nice palette of colors for the heatmap (copper_r, inferno, magma, plasma, viridis, cividis)
plt.show()


X_train_sampled, y_train_sampled = list_X["Alcohol"], list_y["Alcohol"]
y_test_sampled = y_test.loc[:,"Alcohol"]


model_names = ["LogisticRegression", "SVC", "KNeighborsClassifier", "DecisionTreeClassifier", "RandomForestClassifier", "BaggingClassifier", "GradientBoostingClassifier", "SGDClassifier", "BernoulliNB"]
models = [LogisticRegression(max_iter=1000), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), BaggingClassifier(), GradientBoostingClassifier(), SGDClassifier(), BernoulliNB()]

nrows = 3
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(30,15))
k = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train_sampled
        y_test_i = y_test_sampled
        models[k].fit(X_train_sampled, y_train_i)
        preds = models[k].predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True, fmt='.2f',vmin=0, vmax=100, cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + model_names[k])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        k+=1 
fig.tight_layout(pad=3.0)
fig.show()


All_names = pd.DataFrame({drug_name:[basic,weight,sampling] for (drug_name, basic), (_, weight), (_, sampling) in zip(best_model_name_basic.items(),
                                                                                                                     best_model_name_weight.items(),
                                                                                                                     best_model_name_sampling.items())})
All_names.index = ["Basic_model_name","Weight_model_name","Sampling_model_name"]

All_scores = pd.DataFrame([All_scores_basic.iloc[:,:-1].max(), All_scores_weight.iloc[:,:-1].max(), All_scores_sampling.iloc[:,:-1].max()])
All_scores.index = ["Basic","Weight","Sampling"]

best_methods = [All_names.index[np.argmax(All_scores[drug])] for drug in All_scores.columns]
best_methods = pd.DataFrame({drug:[best_method,All_names.loc[best_method,drug]] for drug, best_method in zip(All_scores.columns,best_methods)})
best_methods.index = ["Best method","Best model"]

All_scores = All_scores.append(All_names)
All_scores.append(best_methods)


All_models = pd.DataFrame({drug_name:[basic,weight,sampling] for (drug_name, basic), (_, weight), (_, sampling) in zip(best_model_basic.items(),
                                                                                                                     best_model_weight.items(),
                                                                                                                     best_model_sampling.items())})
All_models.index = ["Basic","Weight","Sampling"]


nrows = 17
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(14,60))
drug_names_ = list(best_model_weight.keys())
i = 0
for r in range(nrows):
    j = 0
    y_train_i = y_train.iloc[:,i]
    y_test_i = y_test.iloc[:,i]
    X_train_sampled, y_train_sampled = list_X[drug_names_[i]], list_y[drug_names_[i]]
    for c in range(ncols):
        model = All_models.iloc[j,i]
        if c!=2:
            model.fit(X_train, y_train_i)
        else:
            model.fit(X_train_sampled, y_train_sampled)
        preds = model.predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True,vmin=0, vmax=100, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix ' + drug_names_[i] + " - " + All_models.index[j])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        j+=1
    i+=1
fig.tight_layout(pad=3.0)
fig.show()


nrows = 6
ncols = 3
fig, axis = plt.subplots(nrows, ncols, figsize=(14,16))
drug_names_ = list(best_model_weight.keys())
i = 0
for r in range(nrows):
    for c in range(ncols):  
        y_train_i = y_train.iloc[:,i]
        y_test_i = y_test.iloc[:,i]

        model = best_model_weight[drug_names_[i]]
        model.fit(X_train, y_train_i)
        preds = model.predict(X_test)
        conf_mat = confusion_matrix(y_test_i, preds)
        conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)
        sns.heatmap(conf_mat_normalized, annot=True,vmin=0, vmax=100, fmt='.2f', cmap='Blues',ax=axis[r,c])
        axis[r,c].set_title('Confusion matrix for ' + drug_names_[i])
        axis[r,c].set_ylabel('True label')
        axis[r,c].set_xlabel('Predicted label')
        i+=1
        if i==17:
          break
fig.tight_layout(pad=3.0)
fig.show()


best_tuned_model = dict()


All_scores.loc["Weight"]

Alcohol      0.629563
Amphet       0.691905
Amyl         0.680091
Benzos       0.743058
Caff         0.602147
Cannabis      0.81445
Coke         0.680911
Crack        0.693601
Esctasy      0.640616
Heroin       0.724158
Ketamine     0.663761
Legalh       0.734438
LSD          0.792161
Meth         0.760893
Mushrooms    0.762485
Nicotine     0.679324
VSA          0.747909
Name: Weight, dtype: object


# We will use the same parameters for the grid search for each drug
model_names = ["LogisticRegression", "SVC", "BernoulliNB"]
models = [LogisticRegression(class_weight="balanced", max_iter=1000), SVC(class_weight="balanced", probability=True), BernoulliNB()]
models_without_params = [LogisticRegression, SVC]
parameters_dict = [
                   {    
                      'penalty':('l2', 'l1', 'elasticnet'),
                      'tol':[1e-3,1e-4,1e-5],
                      'C':[1e-1,1,10],
                      'fit_intercept':(True,False),
                      'intercept_scaling':[1e-1,1,10],
                      'class_weight':['balanced'],
                      'solver':('newton-cg', 'lbfgs','liblinear', 'sag', 'saga'),
                      'multi_class': ['ovr'],
                      #'l1_ratio':[0.1,0.15,0.2,0.25],
                      'max_iter':[10000]
                  },
                  {
                      'kernel':('linear', 'poly', 'rbf', 'sigmoid'),
                      'gamma':('scale', 'auto'),
                      'shrinking':(True,False),
                      'probability':[True],
                      'tol':[1e-2,1e-3,1e-4],
                      'class_weight':["balanced"]
                  },
                  {
                      'alpha':np.arange(1,10,1),
                      'fit_prior':(True,False),
                  }
]


def hyperparam_tuning(y_train, y_test, models_name=model_names, models=models, parameters_dict=parameters_dict):
    """
    Apply grid search on the 3 selected models for a given drug and return all scores for each model
    - Balanced accuracy score on y_test for each original model
    - Balanced accuracy score on y_train for each model
    - Balanced accuracy score on y_test for each model
    - Best parameters for each model

    Parameters
    ----------
    y_train: pandas.core.series.series
        The trainset associated to the drug
    y_test: pandas.core.series.series
        The testset associated to the drug 
    models_name: list
        All model names
    models: list
        All models
    parameters_dict: dict
        All parameters for each model

    Returns
    ----------
    perf_test: dict
        All scores for the drug
    """
    perf_test = dict()
    for name, model in zip(model_names, models):
        model.fit(X_train, y_train)
        preds = model.predict(X_test)
        perf_test[f"Orignal_{name}"] = {
                "test_balanced_accuracy":balanced_accuracy_score(y_test, preds),
        }
    with tqdm(total=len(models)) as bar:
        for name, model, parameters in zip(model_names, models, parameters_dict):
            clf = GridSearchCV(estimator=model,
                              param_grid=parameters,
                              scoring=make_scorer(balanced_accuracy_score))
            clf.fit(X_train, y_train)
            preds = clf.predict(X_test)
            perf_test[name] = {
                "balanced_accuracy":clf.best_score_,
                "test_balanced_accuracy":balanced_accuracy_score(y_test, preds),
                "best_params_":clf.best_params_
            }
            bar.update(1)
    return perf_test


drug_name = "Alcohol"
results_alcohol = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_alcohol

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.58680507 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507
 0.58705163 0.58705163 0.58705163 0.58680507 0.58680507 0.58680507
 0.58680507 0.58680507 0.58680507        nan        nan        nan
        nan        nan        nan 0.60823329 0.60823329 0.60823329
        nan        nan        nan 0.59981653 0.59981653 0.59981653
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58680507 0.58680507 0.58680507
 0.58680507 0.58680507 0.58680507 0.58611493 0.58611493 0.58611493
 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507
        nan        nan        nan        nan        nan        nan
 0.60730306 0.60730306 0.60730306        nan        nan        nan
 0.59981653 0.59981653 0.59981653        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507
 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507 0.58680507
 0.58680507 0.58680507 0.58680507        nan        nan        nan
        nan        nan        nan 0.60213996 0.60009914 0.60009914
        nan        nan        nan 0.59981653 0.59981653 0.59981653
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58705163 0.58705163 0.58705163
 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163
 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163
        nan        nan        nan        nan        nan        nan
 0.60823329 0.60823329 0.60823329        nan        nan        nan
 0.60823329 0.60823329 0.60823329        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163
 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163
 0.58705163 0.58705163 0.58705163        nan        nan        nan
        nan        nan        nan 0.60823329 0.60823329 0.60823329
        nan        nan        nan 0.60823329 0.60823329 0.60823329
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58705163 0.58705163 0.58705163
 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163
 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163 0.58705163
        nan        nan        nan        nan        nan        nan
 0.60823329 0.60823329 0.60823329        nan        nan        nan
 0.60823329 0.60823329 0.60823329        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.59283436 0.59283436 0.59283436 0.59283436 0.59283436 0.59283436
 0.58751244 0.58751244 0.58751244 0.59075102 0.59283436 0.59283436
 0.59283436 0.59283436 0.59283436        nan        nan        nan
        nan        nan        nan 0.59742793 0.59742793 0.59742793
        nan        nan        nan 0.59927332 0.59880821 0.59880821
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.59283436 0.59283436 0.59283436
 0.59283436 0.59283436 0.59283436 0.5916791  0.5916791  0.5916791
 0.59283436 0.59283436 0.59283436 0.59283436 0.59283436 0.59283436
        nan        nan        nan        nan        nan        nan
 0.5990483  0.5990483  0.5990483         nan        nan        nan
 0.59718999 0.59880821 0.59880821        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.59283436 0.59283436 0.59283436 0.59283436 0.59283436 0.59283436
 0.59283436 0.59283436 0.59283436 0.59075102 0.59283436 0.59283436
 0.59283436 0.59283436 0.59283436        nan        nan        nan
        nan        nan        nan 0.59927332 0.59927332 0.59927332
        nan        nan        nan 0.59927332 0.59880821 0.59880821
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58751244 0.58751244 0.58751244
 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244
 0.58704732 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244
        nan        nan        nan        nan        nan        nan
 0.59742793 0.59742793 0.59742793        nan        nan        nan
 0.59742578 0.59742793 0.59742793        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244
 0.58751244 0.58751244 0.58751244 0.58704732 0.58751244 0.58751244
 0.58751244 0.58751244 0.58751244        nan        nan        nan
        nan        nan        nan 0.59742793 0.59742793 0.59742793
        nan        nan        nan 0.59789089 0.59742793 0.59742793
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58751244 0.58751244 0.58751244
 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244
 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244 0.58751244
        nan        nan        nan        nan        nan        nan
 0.59742793 0.59742793 0.59742793        nan        nan        nan
 0.59696281 0.59742793 0.59742793        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813        nan        nan        nan
        nan        nan        nan 0.58750813 0.58750813 0.58750813
        nan        nan        nan 0.58797325 0.58750813 0.58750813
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
        nan        nan        nan        nan        nan        nan
 0.58750813 0.58750813 0.58750813        nan        nan        nan
 0.58797325 0.58750813 0.58750813        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813        nan        nan        nan
        nan        nan        nan 0.58750813 0.58750813 0.58750813
        nan        nan        nan 0.58797325 0.58750813 0.58750813
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58797325 0.58750813 0.58750813
        nan        nan        nan        nan        nan        nan
 0.58750813 0.58750813 0.58750813        nan        nan        nan
 0.58797325 0.58750813 0.58750813        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58797325 0.58750813 0.58750813        nan        nan        nan
        nan        nan        nan 0.58750813 0.58750813 0.58750813
        nan        nan        nan 0.58797325 0.58750813 0.58750813
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.58750813 0.58750813 0.58750813
 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813 0.58750813
 0.58704517 0.58750813 0.58750813 0.58797325 0.58750813 0.58750813
        nan        nan        nan        nan        nan        nan
 0.58750813 0.58750813 0.58750813        nan        nan        nan
 0.58797325 0.58750813 0.58750813        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_alcohol

{'BernoulliNB': {'balanced_accuracy': 0.6087387719945859,
  'best_params_': {'alpha': 3, 'fit_prior': False},
  'test_balanced_accuracy': 0.6104155200880572},
 'LogisticRegression': {'balanced_accuracy': 0.6082332920248202,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.592735277930655},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.5001605210053202},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6295633828655293},
 'Orignal_SVC': {'test_balanced_accuracy': 0.5903962575674189},
 'SVC': {'balanced_accuracy': 0.6111622194097277,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'scale',
   'kernel': 'poly',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.5560906255732894}}


drug_name = "Amphet"
results_amphet = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_amphet

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.74900488 0.74900488 0.74900488 0.74900488 0.74900488 0.74900488
 0.74813532 0.74813532 0.74813532 0.74900488 0.74900488 0.74900488
 0.74900488 0.74900488 0.74900488        nan        nan        nan
        nan        nan        nan 0.74629389 0.74629389 0.74629389
        nan        nan        nan 0.75204759 0.75204759 0.7516128
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74900488 0.74900488 0.74900488
 0.74900488 0.74900488 0.74900488 0.7485701  0.7485701  0.7485701
 0.7485701  0.74900488 0.74900488 0.74900488 0.74900488 0.74900488
        nan        nan        nan        nan        nan        nan
 0.74619158 0.74619158 0.74619158        nan        nan        nan
 0.75204759 0.7516128  0.7516128         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74900488 0.74900488 0.74900488 0.74900488 0.74900488 0.74900488
 0.74900488 0.74900488 0.74900488 0.7485701  0.74900488 0.74900488
 0.74900488 0.74900488 0.74900488        nan        nan        nan
        nan        nan        nan 0.75248237 0.75248237 0.75248237
        nan        nan        nan 0.7516128  0.7516128  0.7516128
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74813532 0.74813532 0.74813532
 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532
 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532
        nan        nan        nan        nan        nan        nan
 0.74629389 0.74629389 0.74629389        nan        nan        nan
 0.74629389 0.74629389 0.74629389        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532
 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532
 0.74813532 0.74813532 0.74813532        nan        nan        nan
        nan        nan        nan 0.74629389 0.74629389 0.74629389
        nan        nan        nan 0.74629389 0.74629389 0.74629389
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74813532 0.74813532 0.74813532
 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532
 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532 0.74813532
        nan        nan        nan        nan        nan        nan
 0.74629389 0.74629389 0.74629389        nan        nan        nan
 0.74629389 0.74629389 0.74629389        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279
 0.74115322 0.74115322 0.74115322 0.74202279 0.74202279 0.74202279
 0.74202279 0.74202279 0.74202279        nan        nan        nan
        nan        nan        nan 0.74028365 0.74028365 0.74028365
        nan        nan        nan 0.74071844 0.74071844 0.74071844
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74202279 0.74202279 0.74202279
 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279
 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279
        nan        nan        nan        nan        nan        nan
 0.74028365 0.74028365 0.74028365        nan        nan        nan
 0.74071844 0.74071844 0.74071844        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279 0.74202279
 0.74202279 0.74202279 0.74202279 0.74245757 0.74202279 0.74202279
 0.74202279 0.74202279 0.74202279        nan        nan        nan
        nan        nan        nan 0.74071844 0.74071844 0.74071844
        nan        nan        nan 0.74071844 0.74071844 0.74071844
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74115322 0.74115322 0.74115322
 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322
 0.741588   0.74115322 0.74115322 0.74115322 0.74115322 0.74115322
        nan        nan        nan        nan        nan        nan
 0.74071844 0.74028365 0.74028365        nan        nan        nan
 0.74028365 0.74028365 0.74028365        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322
 0.74115322 0.74115322 0.74115322 0.741588   0.74115322 0.74115322
 0.74115322 0.74115322 0.74115322        nan        nan        nan
        nan        nan        nan 0.74028365 0.74028365 0.74028365
        nan        nan        nan 0.74028365 0.74028365 0.74028365
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74115322 0.74115322 0.74115322
 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322
 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322 0.74115322
        nan        nan        nan        nan        nan        nan
 0.74028365 0.74028365 0.74028365        nan        nan        nan
 0.74028365 0.74028365 0.74028365        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212        nan        nan        nan
        nan        nan        nan 0.72688212 0.72688212 0.72688212
        nan        nan        nan 0.72688212 0.72688212 0.72688212
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
        nan        nan        nan        nan        nan        nan
 0.72688212 0.72688212 0.72688212        nan        nan        nan
 0.72688212 0.72688212 0.72688212        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212        nan        nan        nan
        nan        nan        nan 0.72688212 0.72688212 0.72688212
        nan        nan        nan 0.72688212 0.72688212 0.72688212
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.7273169  0.72688212 0.72688212
        nan        nan        nan        nan        nan        nan
 0.72688212 0.72688212 0.72688212        nan        nan        nan
 0.72688212 0.72688212 0.72688212        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.7273169  0.72688212 0.72688212        nan        nan        nan
        nan        nan        nan 0.72688212 0.72688212 0.72688212
        nan        nan        nan 0.72688212 0.72688212 0.72688212
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212 0.72688212
        nan        nan        nan        nan        nan        nan
 0.72688212 0.72688212 0.72688212        nan        nan        nan
 0.72688212 0.72688212 0.72688212        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_amphet

{'BernoulliNB': {'balanced_accuracy': 0.7536348136092381,
  'best_params_': {'alpha': 4, 'fit_prior': False},
  'test_balanced_accuracy': 0.6854460093896713},
 'LogisticRegression': {'balanced_accuracy': 0.7524823684414477,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 10,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.6812177412299887},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.641136675123203},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6870498352433441},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6610824366488788},
 'SVC': {'balanced_accuracy': 0.7625970704487328,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'rbf',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.6629049660280524}}


drug_name = "Amyl"
results_amyl = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_amyl

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.69551324 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324
 0.69925323 0.69925323 0.69925323 0.69362645 0.69551324 0.69551324
 0.69551324 0.69551324 0.69551324        nan        nan        nan
        nan        nan        nan 0.69459814 0.69459814 0.69459814
        nan        nan        nan 0.69601994 0.69601994 0.69601994
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69551324 0.69551324 0.69551324
 0.69551324 0.69551324 0.69551324 0.69550204 0.69550204 0.69550204
 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324
        nan        nan        nan        nan        nan        nan
 0.69459814 0.69459814 0.69459814        nan        nan        nan
 0.69601994 0.69601994 0.69601994        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324
 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324 0.69551324
 0.69551324 0.69551324 0.69551324        nan        nan        nan
        nan        nan        nan 0.69649388 0.69649388 0.69649388
        nan        nan        nan 0.69601994 0.69601994 0.69601994
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69925323 0.69925323 0.69925323
 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323
 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323
        nan        nan        nan        nan        nan        nan
 0.69459814 0.69459814 0.69459814        nan        nan        nan
 0.69459814 0.69459814 0.69459814        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323
 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323
 0.69972716 0.69925323 0.69925323        nan        nan        nan
        nan        nan        nan 0.69459814 0.69459814 0.69459814
        nan        nan        nan 0.69459814 0.69459814 0.69459814
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69925323 0.69925323 0.69925323
 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323
 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323 0.69925323
        nan        nan        nan        nan        nan        nan
 0.69459814 0.69459814 0.69459814        nan        nan        nan
 0.69507207 0.69459814 0.69459814        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254
 0.69843349 0.69843349 0.69843349 0.70032254 0.70032254 0.70032254
 0.70032254 0.70032254 0.70032254        nan        nan        nan
        nan        nan        nan 0.69746188 0.69746188 0.69746188
        nan        nan        nan 0.69652522 0.69652522 0.69652522
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70032254 0.70032254 0.70032254
 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254
 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254
        nan        nan        nan        nan        nan        nan
 0.69746188 0.69746188 0.69746188        nan        nan        nan
 0.69699915 0.69699915 0.69652522        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254
 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254 0.70032254
 0.70032254 0.70032254 0.70032254        nan        nan        nan
        nan        nan        nan 0.69652522 0.69652522 0.69652522
        nan        nan        nan 0.69652522 0.69699915 0.69652522
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69843349 0.69843349 0.69843349
 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349
 0.69795956 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349
        nan        nan        nan        nan        nan        nan
 0.69746188 0.69746188 0.69746188        nan        nan        nan
 0.69746188 0.69746188 0.69746188        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349
 0.69843349 0.69843349 0.69843349 0.69795956 0.69843349 0.69843349
 0.69843349 0.69843349 0.69843349        nan        nan        nan
        nan        nan        nan 0.69746188 0.69746188 0.69746188
        nan        nan        nan 0.69746188 0.69746188 0.69746188
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69843349 0.69843349 0.69843349
 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349
 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349 0.69843349
        nan        nan        nan        nan        nan        nan
 0.69746188 0.69746188 0.69746188        nan        nan        nan
 0.69746188 0.69746188 0.69746188        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773
 0.7017838  0.7017838  0.7017838  0.70225773 0.70225773 0.70225773
 0.70225773 0.70225773 0.70225773        nan        nan        nan
        nan        nan        nan 0.7017838  0.7017838  0.7017838
        nan        nan        nan 0.70225773 0.70225773 0.70225773
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70225773 0.70225773 0.70225773
 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773
 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773
        nan        nan        nan        nan        nan        nan
 0.699897   0.7017838  0.7017838         nan        nan        nan
 0.70225773 0.70225773 0.70225773        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773
 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773 0.70225773
 0.70225773 0.70225773 0.70225773        nan        nan        nan
        nan        nan        nan 0.7017838  0.70225773 0.70225773
        nan        nan        nan 0.70225773 0.70225773 0.70225773
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7017838  0.7017838  0.7017838
 0.7017838  0.7017838  0.7017838  0.7017838  0.7017838  0.7017838
 0.7017838  0.7017838  0.7017838  0.7017838  0.7017838  0.7017838
        nan        nan        nan        nan        nan        nan
 0.7017838  0.699897   0.7017838         nan        nan        nan
 0.7017838  0.699897   0.699897          nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.7017838  0.7017838  0.7017838  0.7017838  0.7017838  0.7017838
 0.7017838  0.7017838  0.7017838  0.7017838  0.7017838  0.7017838
 0.7017838  0.7017838  0.7017838         nan        nan        nan
        nan        nan        nan 0.7017838  0.7017838  0.7017838
        nan        nan        nan 0.7017838  0.699897   0.699897
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7017838  0.7017838  0.7017838
 0.7017838  0.7017838  0.7017838  0.7017838  0.7017838  0.7017838
 0.7017838  0.7017838  0.7017838  0.7017838  0.7017838  0.7017838
        nan        nan        nan        nan        nan        nan
 0.7017838  0.7017838  0.7017838         nan        nan        nan
 0.7017838  0.699897   0.699897          nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_amyl

{'BernoulliNB': {'balanced_accuracy': 0.6692415074468191,
  'best_params_': {'alpha': 3, 'fit_prior': False},
  'test_balanced_accuracy': 0.6537629037629038},
 'LogisticRegression': {'balanced_accuracy': 0.7022577305783958,
  'best_params_': {'C': 10,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'newton-cg',
   'tol': 0.001},
  'test_balanced_accuracy': 0.6779262404262405},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.539044289044289},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6800907425907425},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6716824841824842},
 'SVC': {'balanced_accuracy': 0.6942782530361893,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'scale',
   'kernel': 'linear',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.66999666999667}}


drug_name = "Benzos"
results_benzos = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_benzos

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.69044671 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671
 0.69204935 0.69204935 0.69204935 0.69044671 0.69044671 0.69044671
 0.69044671 0.69044671 0.69044671        nan        nan        nan
        nan        nan        nan 0.69648339 0.69648339 0.69648339
        nan        nan        nan 0.69705846 0.69705846 0.69705846
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69044671 0.69044671 0.69044671
 0.69044671 0.69044671 0.69044671 0.69237722 0.69237722 0.69237722
 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671
        nan        nan        nan        nan        nan        nan
 0.69648339 0.69648339 0.69648339        nan        nan        nan
 0.69705846 0.69705846 0.69705846        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671
 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671 0.69044671
 0.69044671 0.69044671 0.69044671        nan        nan        nan
        nan        nan        nan 0.69512796 0.69512796 0.69512796
        nan        nan        nan 0.69705846 0.69705846 0.69705846
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69204935 0.69204935 0.69204935
 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935
 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935
        nan        nan        nan        nan        nan        nan
 0.69648339 0.69648339 0.69648339        nan        nan        nan
 0.69648339 0.69648339 0.69648339        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935
 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935
 0.69204935 0.69204935 0.69204935        nan        nan        nan
        nan        nan        nan 0.69648339 0.69648339 0.69648339
        nan        nan        nan 0.69648339 0.69648339 0.69648339
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69204935 0.69204935 0.69204935
 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935
 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935 0.69204935
        nan        nan        nan        nan        nan        nan
 0.69648339 0.69648339 0.69648339        nan        nan        nan
 0.69648339 0.69648339 0.69648339        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.6838056  0.6838056  0.6838056  0.6838056  0.6838056  0.6838056
 0.68902    0.68902    0.68902    0.6838056  0.6838056  0.6838056
 0.6838056  0.6838056  0.6838056         nan        nan        nan
        nan        nan        nan 0.68811502 0.68811502 0.68811502
        nan        nan        nan 0.69140095 0.69140095 0.69140095
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.6838056  0.6838056  0.6838056
 0.6838056  0.6838056  0.6838056  0.6838056  0.6838056  0.6838056
 0.6838056  0.6838056  0.6838056  0.6838056  0.6838056  0.6838056
        nan        nan        nan        nan        nan        nan
 0.68811502 0.68811502 0.68811502        nan        nan        nan
 0.69140095 0.69140095 0.69140095        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.6838056  0.6838056  0.6838056  0.6838056  0.6838056  0.6838056
 0.6838056  0.6838056  0.6838056  0.6838056  0.6838056  0.6838056
 0.6838056  0.6838056  0.6838056         nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
        nan        nan        nan 0.69140095 0.69140095 0.69140095
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
        nan        nan        nan        nan        nan        nan
 0.68811502 0.68811502 0.68811502        nan        nan        nan
 0.68811502 0.68811502 0.68811502        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902           nan        nan        nan
        nan        nan        nan 0.68811502 0.68811502 0.68811502
        nan        nan        nan 0.68811502 0.68811502 0.68811502
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
        nan        nan        nan        nan        nan        nan
 0.68811502 0.68811502 0.68811502        nan        nan        nan
 0.68811502 0.68811502 0.68811502        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68947248 0.68902    0.68902           nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
        nan        nan        nan 0.68902    0.68856751 0.68856751
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68947248 0.68902    0.68902
        nan        nan        nan        nan        nan        nan
 0.68902    0.68902    0.68902           nan        nan        nan
 0.68902    0.68856751 0.68856751        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68947248 0.68902    0.68902           nan        nan        nan
        nan        nan        nan 0.69094846 0.68856751 0.68856751
        nan        nan        nan 0.68902    0.68856751 0.68856751
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
        nan        nan        nan        nan        nan        nan
 0.69140095 0.68902    0.68902           nan        nan        nan
 0.68947248 0.68902    0.68902           nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902           nan        nan        nan
        nan        nan        nan 0.69140095 0.68902    0.68902
        nan        nan        nan 0.68947248 0.68902    0.68902
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
 0.68902    0.68902    0.68902    0.68902    0.68902    0.68902
        nan        nan        nan        nan        nan        nan
 0.68902    0.68902    0.68902           nan        nan        nan
 0.68947248 0.68902    0.68902           nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_benzos

{'BernoulliNB': {'balanced_accuracy': 0.6818543314439347,
  'best_params_': {'alpha': 4, 'fit_prior': False},
  'test_balanced_accuracy': 0.731694560669456},
 'LogisticRegression': {'balanced_accuracy': 0.697058464637124,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'saga',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7321462533282617},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.696819132750095},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.7430581970330924},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6935621909471281},
 'SVC': {'balanced_accuracy': 0.6848828609430525,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'scale',
   'kernel': 'linear',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.729602510460251}}


drug_name = "Caff"
results_caff = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_caff

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.55599885 0.55599885 0.55599885 0.55599885 0.55599885 0.55599885
 0.56007904 0.56007904 0.56007904 0.55410543 0.55599885 0.55599885
 0.55462899 0.55599885 0.55599885        nan        nan        nan
        nan        nan        nan 0.56614944 0.566673   0.566673
        nan        nan        nan 0.5704816  0.57100516 0.57100516
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.55599885 0.55599885 0.55599885
 0.55599885 0.55599885 0.55599885 0.55815296 0.55815296 0.55815296
 0.55599885 0.55599885 0.55599885 0.55462899 0.55599885 0.55599885
        nan        nan        nan        nan        nan        nan
 0.566673   0.566673   0.566673          nan        nan        nan
 0.57100516 0.57100516 0.57100516        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.55599885 0.55599885 0.55599885 0.55599885 0.55599885 0.55599885
 0.55462899 0.55462899 0.55462899 0.55599885 0.55599885 0.55599885
 0.55599885 0.55599885 0.55599885        nan        nan        nan
        nan        nan        nan 0.56824369 0.56824369 0.56824369
        nan        nan        nan 0.57100516 0.57100516 0.57100516
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.56007904 0.56007904 0.56007904
 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904
 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904
        nan        nan        nan        nan        nan        nan
 0.566673   0.566673   0.566673          nan        nan        nan
 0.56562588 0.566673   0.566673          nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904
 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904
 0.56007904 0.56007904 0.56007904        nan        nan        nan
        nan        nan        nan 0.566673   0.566673   0.566673
        nan        nan        nan 0.566673   0.566673   0.566673
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.56007904 0.56007904 0.56007904
 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904
 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904 0.56007904
        nan        nan        nan        nan        nan        nan
 0.566673   0.566673   0.566673          nan        nan        nan
 0.566673   0.566673   0.566673          nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737
 0.5440364  0.54264751 0.5440364  0.54455723 0.54318737 0.54318737
 0.54318737 0.54318737 0.54318737        nan        nan        nan
        nan        nan        nan 0.54470636 0.54470636 0.54470636
        nan        nan        nan 0.54175225 0.54175225 0.54175225
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.54318737 0.54318737 0.54318737
 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737
 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737
        nan        nan        nan        nan        nan        nan
 0.54470636 0.54470636 0.54470636        nan        nan        nan
 0.54175225 0.54175225 0.54175225        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737 0.54318737
 0.54318737 0.54318737 0.54318737 0.54455723 0.54318737 0.54318737
 0.54318737 0.54318737 0.54318737        nan        nan        nan
        nan        nan        nan 0.54227581 0.54227581 0.54227581
        nan        nan        nan 0.54175225 0.54175225 0.54175225
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.54264751 0.54264751 0.54264751
 0.54264751 0.54264751 0.54264751 0.5440364  0.54264751 0.54264751
 0.54264751 0.54264751 0.54264751 0.54264751 0.54264751 0.54264751
        nan        nan        nan        nan        nan        nan
 0.54470636 0.54470636 0.54470636        nan        nan        nan
 0.54470636 0.54470636 0.54470636        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.54264751 0.54264751 0.54264751 0.54264751 0.54264751 0.54264751
 0.5440364  0.54264751 0.54264751 0.54264751 0.54264751 0.54264751
 0.54264751 0.54264751 0.54264751        nan        nan        nan
        nan        nan        nan 0.54470636 0.54470636 0.54470636
        nan        nan        nan 0.54470636 0.54470636 0.54470636
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.54264751 0.54264751 0.54264751
 0.54264751 0.54264751 0.54264751 0.5440364  0.54264751 0.54264751
 0.54401737 0.54264751 0.54264751 0.54264751 0.54264751 0.54264751
        nan        nan        nan        nan        nan        nan
 0.54470636 0.54470636 0.54470636        nan        nan        nan
 0.54522992 0.54470636 0.54470636        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.54338818 0.54338818 0.54338818 0.54391174 0.54391174 0.54391174
 0.54338818 0.54391174 0.54391174 0.54338818 0.54338818 0.54338818
 0.54391174 0.54338818 0.54338818        nan        nan        nan
        nan        nan        nan 0.54697421 0.54697421 0.54697421
        nan        nan        nan 0.54697421 0.54645065 0.54645065
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.54338818 0.54338818 0.54338818
 0.54391174 0.54391174 0.54391174 0.54338818 0.54338818 0.54338818
 0.54286462 0.54338818 0.54338818 0.54391174 0.54338818 0.54338818
        nan        nan        nan        nan        nan        nan
 0.54697421 0.54697421 0.54697421        nan        nan        nan
 0.54697421 0.54645065 0.54645065        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.54338818 0.54338818 0.54338818 0.54391174 0.54391174 0.54391174
 0.54338818 0.54338818 0.54338818 0.54338818 0.54338818 0.54338818
 0.54391174 0.54338818 0.54338818        nan        nan        nan
        nan        nan        nan 0.54645065 0.54645065 0.54645065
        nan        nan        nan 0.54697421 0.54645065 0.54645065
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.54391174 0.54391174 0.54391174
 0.54391174 0.54391174 0.54391174 0.54338818 0.54391174 0.54391174
 0.54391174 0.54391174 0.54391174 0.54391174 0.54391174 0.54391174
        nan        nan        nan        nan        nan        nan
 0.54697421 0.54697421 0.54697421        nan        nan        nan
 0.54749777 0.54697421 0.54697421        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.54391174 0.54391174 0.54391174 0.54391174 0.54391174 0.54391174
 0.54338818 0.54391174 0.54391174 0.54338818 0.54391174 0.54391174
 0.54338818 0.54391174 0.54391174        nan        nan        nan
        nan        nan        nan 0.54697421 0.54697421 0.54697421
        nan        nan        nan 0.54749777 0.54697421 0.54697421
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.54391174 0.54391174 0.54391174
 0.54391174 0.54391174 0.54391174 0.54338818 0.54391174 0.54391174
 0.54338818 0.54391174 0.54391174 0.54338818 0.54391174 0.54391174
        nan        nan        nan        nan        nan        nan
 0.54697421 0.54697421 0.54697421        nan        nan        nan
 0.54749777 0.54697421 0.54697421        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_caff

{'BernoulliNB': {'balanced_accuracy': 0.5538812038298788,
  'best_params_': {'alpha': 4, 'fit_prior': False},
  'test_balanced_accuracy': 0.5891736001225052},
 'LogisticRegression': {'balanced_accuracy': 0.5710051638815177,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'saga',
   'tol': 0.0001},
  'test_balanced_accuracy': 0.5692069487689926},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.505274530821976},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6021472444830109},
 'Orignal_SVC': {'test_balanced_accuracy': 0.5543021455430215},
 'SVC': {'balanced_accuracy': 0.5703482980308718,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': False,
   'tol': 0.01},
  'test_balanced_accuracy': 0.6008881629319586}}


drug_name = "Cannabis"
results_cannabis = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_cannabis

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.79236921 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921
 0.7921219  0.7921219  0.7921219  0.79236921 0.79236921 0.79236921
 0.79236921 0.79236921 0.79236921        nan        nan        nan
        nan        nan        nan 0.78766319 0.78766319 0.78766319
        nan        nan        nan 0.7848689  0.7848689  0.7848689
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79236921 0.79236921 0.79236921
 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921
 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921
        nan        nan        nan        nan        nan        nan
 0.79013496 0.78882777 0.78882777        nan        nan        nan
 0.7848689  0.7848689  0.7848689         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921
 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921 0.79236921
 0.79236921 0.79236921 0.79236921        nan        nan        nan
        nan        nan        nan 0.78617609 0.78617609 0.78617609
        nan        nan        nan 0.7848689  0.7848689  0.7848689
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7921219  0.7921219  0.7921219
 0.7921219  0.7921219  0.7921219  0.7921219  0.7921219  0.7921219
 0.7921219  0.7921219  0.7921219  0.7921219  0.7921219  0.7921219
        nan        nan        nan        nan        nan        nan
 0.78831678 0.78766319 0.78766319        nan        nan        nan
 0.78766319 0.78766319 0.78766319        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.7921219  0.7921219  0.7921219  0.7921219  0.7921219  0.7921219
 0.7921219  0.7921219  0.7921219  0.7921219  0.7921219  0.7921219
 0.7921219  0.7921219  0.7921219         nan        nan        nan
        nan        nan        nan 0.78831678 0.78766319 0.78766319
        nan        nan        nan 0.78766319 0.78766319 0.78766319
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7921219  0.7921219  0.7921219
 0.7921219  0.7921219  0.7921219  0.7921219  0.7921219  0.7921219
 0.7921219  0.7921219  0.7921219  0.7921219  0.7921219  0.7921219
        nan        nan        nan        nan        nan        nan
 0.7867541  0.78766319 0.78766319        nan        nan        nan
 0.78766319 0.78766319 0.78766319        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961
 0.79031191 0.79031191 0.78965831 0.78835961 0.78835961 0.78835961
 0.78835961 0.78835961 0.78835961        nan        nan        nan
        nan        nan        nan 0.78785257 0.78876166 0.78876166
        nan        nan        nan 0.78680087 0.78680087 0.78680087
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78835961 0.78835961 0.78835961
 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961
 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961
        nan        nan        nan        nan        nan        nan
 0.78810806 0.78810806 0.78745447        nan        nan        nan
 0.78680087 0.78680087 0.78680087        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961
 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961 0.78835961
 0.78835961 0.78835961 0.78835961        nan        nan        nan
        nan        nan        nan 0.78680087 0.78680087 0.78680087
        nan        nan        nan 0.78680087 0.78680087 0.78680087
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79031191 0.79031191 0.79031191
 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191
 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191
        nan        nan        nan        nan        nan        nan
 0.78785257 0.78876166 0.78876166        nan        nan        nan
 0.78876166 0.78876166 0.78876166        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191
 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191
 0.79031191 0.79031191 0.79031191        nan        nan        nan
        nan        nan        nan 0.78785257 0.78876166 0.78876166
        nan        nan        nan 0.78876166 0.78876166 0.78876166
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79031191 0.79031191 0.79031191
 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191
 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191 0.79031191
        nan        nan        nan        nan        nan        nan
 0.78785257 0.78876166 0.78876166        nan        nan        nan
 0.78876166 0.78876166 0.78876166        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333        nan        nan        nan
        nan        nan        nan 0.78745052 0.78745052 0.78745052
        nan        nan        nan 0.78679693 0.78679693 0.78679693
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
        nan        nan        nan        nan        nan        nan
 0.78745052 0.78745052 0.78745052        nan        nan        nan
 0.78679693 0.78679693 0.78679693        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333        nan        nan        nan
        nan        nan        nan 0.78745052 0.78679693 0.78679693
        nan        nan        nan 0.78679693 0.78679693 0.78679693
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
        nan        nan        nan        nan        nan        nan
 0.78745052 0.78745052 0.78745052        nan        nan        nan
 0.78745052 0.78745052 0.78745052        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333        nan        nan        nan
        nan        nan        nan 0.78745052 0.78745052 0.78745052
        nan        nan        nan 0.78745052 0.78745052 0.78745052
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333 0.78614333
        nan        nan        nan        nan        nan        nan
 0.78810412 0.78745052 0.78745052        nan        nan        nan
 0.78745052 0.78745052 0.78745052        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_cannabis

{'BernoulliNB': {'balanced_accuracy': 0.7903155561979093,
  'best_params_': {'alpha': 1, 'fit_prior': False},
  'test_balanced_accuracy': 0.8029825801889825},
 'LogisticRegression': {'balanced_accuracy': 0.7923692064868535,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'newton-cg',
   'tol': 0.001},
  'test_balanced_accuracy': 0.8129395127595295},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.7977694928328084},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.8144500867776563},
 'Orignal_SVC': {'test_balanced_accuracy': 0.7978980523237128},
 'SVC': {'balanced_accuracy': 0.7954402989697107,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.001},
  'test_balanced_accuracy': 0.8108118531850614}}


drug_name = "Coke"
results_coke = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_coke

/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  ConvergenceWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.70518883 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883
 0.702091   0.702091   0.702091   0.70560549 0.70518883 0.70518883
 0.70560549 0.70518883 0.70518883        nan        nan        nan
        nan        nan        nan 0.69132758 0.69091264 0.69091264
        nan        nan        nan 0.69692367 0.69733861 0.69733861
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70518883 0.70518883 0.70518883
 0.70518883 0.70518883 0.70518883 0.70643883 0.70643883 0.70643883
 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883
        nan        nan        nan        nan        nan        nan
 0.69091264 0.69091264 0.69091264        nan        nan        nan
 0.69733861 0.69733861 0.69733861        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883
 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883 0.70518883
 0.70518883 0.70518883 0.70518883        nan        nan        nan
        nan        nan        nan 0.69340918 0.69340918 0.69340918
        nan        nan        nan 0.69775528 0.69733861 0.69733861
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.702091   0.702091   0.702091
 0.702091   0.702091   0.702091   0.702091   0.702091   0.702091
 0.702091   0.702091   0.702091   0.702091   0.702091   0.702091
        nan        nan        nan        nan        nan        nan
 0.69091264 0.69091264 0.69091264        nan        nan        nan
 0.69132758 0.69091264 0.69091264        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.702091   0.702091   0.702091   0.702091   0.702091   0.702091
 0.702091   0.702091   0.702091   0.702091   0.702091   0.702091
 0.702091   0.702091   0.702091          nan        nan        nan
        nan        nan        nan 0.69091264 0.69091264 0.69091264
        nan        nan        nan 0.69091264 0.69091264 0.69091264
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.702091   0.702091   0.702091
 0.702091   0.702091   0.702091   0.702091   0.702091   0.702091
 0.702091   0.702091   0.702091   0.702091   0.702091   0.702091
        nan        nan        nan        nan        nan        nan
 0.69132758 0.69091264 0.69091264        nan        nan        nan
 0.69091264 0.69091264 0.69091264        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70671831 0.70671831 0.70671831 0.70671831 0.70671831 0.70671831
 0.70630337 0.70630337 0.70630337 0.69078831 0.69638095 0.70260501
 0.70671831 0.70671831 0.70671831        nan        nan        nan
        nan        nan        nan 0.70713498 0.70713498 0.70713498
        nan        nan        nan 0.70713325 0.70754818 0.70754818
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70671831 0.70671831 0.70671831
 0.70671831 0.70671831 0.70671831 0.70671831 0.70671831 0.70671831
 0.70671831 0.69699433 0.70899145 0.70671831 0.70671831 0.70671831
        nan        nan        nan        nan        nan        nan
 0.70754991 0.70713498 0.70713498        nan        nan        nan
 0.70713325 0.70754818 0.70754818        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70671831 0.70671831 0.70671831 0.70671831 0.70671831 0.70671831
 0.70671831 0.70671831 0.70671831 0.69782421 0.70444517 0.69823915
 0.70671831 0.70671831 0.70671831        nan        nan        nan
        nan        nan        nan 0.70754818 0.70754818 0.70754818
        nan        nan        nan 0.70713325 0.70754818 0.70754818
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70630337 0.70630337 0.70630337
 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337
 0.70671831 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337
        nan        nan        nan        nan        nan        nan
 0.70713498 0.70713498 0.70713498        nan        nan        nan
 0.70713498 0.70713498 0.70713498        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337
 0.70630337 0.70630337 0.70630337 0.70671831 0.70630337 0.70630337
 0.70630337 0.70630337 0.70630337        nan        nan        nan
        nan        nan        nan 0.70713498 0.70713498 0.70713498
        nan        nan        nan 0.70713498 0.70713498 0.70713498
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70630337 0.70630337 0.70630337
 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337 0.70630337
 0.70713498 0.70630337 0.70671831 0.70630337 0.70630337 0.70630337
        nan        nan        nan        nan        nan        nan
 0.70713498 0.70713498 0.70713498        nan        nan        nan
 0.70713498 0.70713498 0.70713498        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184
 0.70486184 0.70486184 0.70486184 0.68624549 0.6988723  0.66989882
 0.70444517 0.70486184 0.70486184        nan        nan        nan
        nan        nan        nan 0.708793   0.708793   0.708793
        nan        nan        nan 0.70486184 0.70486184 0.70486184
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70486184 0.70486184 0.70486184
 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184
 0.69472292 0.70506029 0.69118693 0.70444517 0.70486184 0.70486184
        nan        nan        nan        nan        nan        nan
 0.708793   0.708793   0.708793          nan        nan        nan
 0.70486184 0.70486184 0.70486184        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184
 0.70486184 0.70486184 0.70486184 0.7100215  0.70175882 0.68725404
 0.70444517 0.70486184 0.70486184        nan        nan        nan
        nan        nan        nan 0.70920966 0.70486184 0.70486184
        nan        nan        nan 0.70486184 0.70486184 0.70486184
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70486184 0.70486184 0.70486184
 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184
 0.70031556 0.68996016 0.69762749 0.70444517 0.70444517 0.70486184
        nan        nan        nan        nan        nan        nan
 0.708793   0.708793   0.708793          nan        nan        nan
 0.70920966 0.708793   0.708793          nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184
 0.70486184 0.70486184 0.70486184 0.70444517 0.71086941 0.70215572
 0.70444517 0.70444517 0.70486184        nan        nan        nan
        nan        nan        nan 0.708793   0.708793   0.708793
        nan        nan        nan 0.70920966 0.708793   0.708793
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.70486184 0.70486184 0.70486184
 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184 0.70486184
 0.70029579 0.69430799 0.68790351 0.70444517 0.70444517 0.70486184
        nan        nan        nan        nan        nan        nan
 0.708793   0.708793   0.708793          nan        nan        nan
 0.70920966 0.708793   0.708793          nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_coke

{'BernoulliNB': {'balanced_accuracy': 0.6938275753202238,
  'best_params_': {'alpha': 5, 'fit_prior': False},
  'test_balanced_accuracy': 0.6512739561563431},
 'LogisticRegression': {'balanced_accuracy': 0.7108694148776233,
  'best_params_': {'C': 10,
   'class_weight': 'balanced',
   'fit_intercept': False,
   'intercept_scaling': 1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'sag',
   'tol': 0.0001},
  'test_balanced_accuracy': 0.6656143003246031},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.5278580639423718},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6637022544355018},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6809106674374139},
 'SVC': {'balanced_accuracy': 0.7150343526369595,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'rbf',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.6831339766107876}}


drug_name = "Crack"
results_crack = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_crack

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.68360941 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941
 0.68692609 0.68692609 0.68692609 0.68360941 0.68360941 0.68360941
 0.68360941 0.68360941 0.68360941        nan        nan        nan
        nan        nan        nan 0.69944677 0.69944677 0.69944677
        nan        nan        nan 0.69480407 0.69480407 0.69480407
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68360941 0.68360941 0.68360941
 0.68360941 0.68360941 0.68360941 0.68148536 0.68148536 0.68148536
 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941
        nan        nan        nan        nan        nan        nan
 0.69944677 0.69944677 0.69944677        nan        nan        nan
 0.69825234 0.69480407 0.69480407        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941
 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941 0.68360941
 0.68360941 0.68360941 0.68360941        nan        nan        nan
        nan        nan        nan 0.69740128 0.69740128 0.69740128
        nan        nan        nan 0.69480407 0.69480407 0.69480407
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68692609 0.68692609 0.68692609
 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609
 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609
        nan        nan        nan        nan        nan        nan
 0.69944677 0.69944677 0.69944677        nan        nan        nan
 0.69944677 0.69944677 0.69944677        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609
 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609
 0.68692609 0.68692609 0.68692609        nan        nan        nan
        nan        nan        nan 0.69944677 0.69944677 0.69944677
        nan        nan        nan 0.69944677 0.69944677 0.69944677
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68692609 0.68692609 0.68692609
 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609
 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609 0.68692609
        nan        nan        nan        nan        nan        nan
 0.69944677 0.69944677 0.69944677        nan        nan        nan
 0.69944677 0.69944677 0.69944677        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139
 0.67356876 0.67356876 0.67356876 0.67212139 0.67212139 0.67212139
 0.67212139 0.67212139 0.67212139        nan        nan        nan
        nan        nan        nan 0.67544167 0.67544167 0.67544167
        nan        nan        nan 0.67484356 0.67484356 0.67484356
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.67212139 0.67212139 0.67212139
 0.67212139 0.67212139 0.67212139 0.67127032 0.67127032 0.67127032
 0.67169586 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139
        nan        nan        nan        nan        nan        nan
 0.6727195  0.67229577 0.67229577        nan        nan        nan
 0.67484356 0.67484356 0.67484356        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139
 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139 0.67212139
 0.67212139 0.67212139 0.67212139        nan        nan        nan
        nan        nan        nan 0.67441802 0.67484356 0.67484356
        nan        nan        nan 0.67484356 0.67484356 0.67484356
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.67356876 0.67356876 0.67356876
 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876
 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876
        nan        nan        nan        nan        nan        nan
 0.67544167 0.67544167 0.67544167        nan        nan        nan
 0.67544167 0.67544167 0.67544167        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876
 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876
 0.67356876 0.67356876 0.67356876        nan        nan        nan
        nan        nan        nan 0.67544167 0.67544167 0.67544167
        nan        nan        nan 0.67544167 0.67544167 0.67544167
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.67356876 0.67356876 0.67356876
 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876
 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876 0.67356876
        nan        nan        nan        nan        nan        nan
 0.67544167 0.67544167 0.67544167        nan        nan        nan
 0.67544167 0.67544167 0.67544167        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344
 0.66994791 0.66994791 0.66994791 0.67037344 0.67037344 0.67037344
 0.67037344 0.67037344 0.67037344        nan        nan        nan
        nan        nan        nan 0.67309561 0.67309561 0.67309561
        nan        nan        nan 0.66994971 0.66994971 0.66952418
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.67037344 0.67037344 0.67037344
 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344
 0.66994971 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344
        nan        nan        nan        nan        nan        nan
 0.66952418 0.66952418 0.66952418        nan        nan        nan
 0.66994971 0.66994971 0.66952418        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344
 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344 0.67037344
 0.67037344 0.67037344 0.67037344        nan        nan        nan
        nan        nan        nan 0.66952418 0.66952418 0.66952418
        nan        nan        nan 0.66994971 0.66994971 0.66952418
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.66994791 0.66994791 0.66994791
 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791
 0.66994791 0.66994791 0.66994791 0.6699461  0.66994791 0.66994791
        nan        nan        nan        nan        nan        nan
 0.67309561 0.67309561 0.67309561        nan        nan        nan
 0.67351933 0.67309561 0.67309561        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791
 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791
 0.6699461  0.66994791 0.66994791        nan        nan        nan
        nan        nan        nan 0.67309561 0.67309561 0.67309561
        nan        nan        nan 0.67351933 0.67309561 0.67309561
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.66994791 0.66994791 0.66994791
 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791 0.66994791
 0.67037163 0.66994791 0.66994791 0.6699461  0.66994791 0.66994791
        nan        nan        nan        nan        nan        nan
 0.67309561 0.67309561 0.67309561        nan        nan        nan
 0.67351933 0.67309561 0.67309561        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_crack

{'BernoulliNB': {'balanced_accuracy': 0.6819046257099156,
  'best_params_': {'alpha': 1, 'fit_prior': False},
  'test_balanced_accuracy': 0.6665219279201042},
 'LogisticRegression': {'balanced_accuracy': 0.6994467676521844,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7121738443926893},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.6548573007539573},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6927327991157779},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6936012315951525},
 'SVC': {'balanced_accuracy': 0.6723013612970961,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.001},
  'test_balanced_accuracy': 0.6922985828760905}}


drug_name = "Esctasy"
results_esctasy = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_esctasy

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.7560619  0.7560619  0.7560619  0.7560619  0.7560619  0.7560619
 0.75033503 0.75347826 0.75347826 0.7560619  0.7560619  0.7560619
 0.7560619  0.7560619  0.7560619         nan        nan        nan
        nan        nan        nan 0.75185075 0.75185075 0.75185075
        nan        nan        nan 0.74588575 0.74544522 0.74544522
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7560619  0.7560619  0.7560619
 0.7560619  0.7560619  0.7560619  0.7529782  0.7529782  0.7529782
 0.7560619  0.7560619  0.7560619  0.75562137 0.7560619  0.7560619
        nan        nan        nan        nan        nan        nan
 0.75229128 0.75229128 0.75229128        nan        nan        nan
 0.74544522 0.74544522 0.74544522        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.7560619  0.7560619  0.7560619  0.7560619  0.7560619  0.7560619
 0.7560619  0.7560619  0.7560619  0.7560619  0.7560619  0.7560619
 0.75562137 0.7560619  0.7560619         nan        nan        nan
        nan        nan        nan 0.74544522 0.7450047  0.7450047
        nan        nan        nan 0.74544522 0.74544522 0.74544522
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75303773 0.75303773 0.75303773
 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773
 0.75347826 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773
        nan        nan        nan        nan        nan        nan
 0.75185075 0.75185075 0.75185075        nan        nan        nan
 0.75185075 0.75185075 0.75185075        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773
 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773
 0.75303773 0.75303773 0.75303773        nan        nan        nan
        nan        nan        nan 0.75185075 0.75185075 0.75185075
        nan        nan        nan 0.75185075 0.75185075 0.75185075
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75303773 0.75303773 0.75303773
 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773
 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773 0.75303773
        nan        nan        nan        nan        nan        nan
 0.75185075 0.75185075 0.75185075        nan        nan        nan
 0.75185075 0.75185075 0.75185075        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479
 0.74927538 0.74927538 0.74927538 0.74833479 0.74833479 0.74833479
 0.74833479 0.74833479 0.74833479        nan        nan        nan
        nan        nan        nan 0.74789426 0.74833479 0.74833479
        nan        nan        nan 0.74519156 0.74563209 0.74563209
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74833479 0.74833479 0.74833479
 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479
 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479
        nan        nan        nan        nan        nan        nan
 0.74745373 0.74519156 0.74519156        nan        nan        nan
 0.74519156 0.74563209 0.74563209        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479 0.74833479
 0.74833479 0.74833479 0.74833479 0.74789426 0.74833479 0.74833479
 0.74833479 0.74833479 0.74833479        nan        nan        nan
        nan        nan        nan 0.74563209 0.74563209 0.74563209
        nan        nan        nan 0.74519156 0.74563209 0.74563209
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74927538 0.74927538 0.74927538
 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538
 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538
        nan        nan        nan        nan        nan        nan
 0.74833479 0.74833479 0.74833479        nan        nan        nan
 0.74745373 0.74833479 0.74833479        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538
 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538
 0.74927538 0.74927538 0.74927538        nan        nan        nan
        nan        nan        nan 0.74833479 0.74833479 0.74833479
        nan        nan        nan 0.74745373 0.74833479 0.74833479
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74927538 0.74927538 0.74927538
 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538
 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538 0.74927538
        nan        nan        nan        nan        nan        nan
 0.74833479 0.74833479 0.74833479        nan        nan        nan
 0.74745373 0.74833479 0.74833479        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802
 0.75103749 0.75103749 0.75103749 0.75147802 0.75147802 0.75147802
 0.75147802 0.75147802 0.75147802        nan        nan        nan
        nan        nan        nan 0.74877532 0.74877532 0.74877532
        nan        nan        nan 0.75147802 0.75191855 0.75191855
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75147802 0.75147802 0.75147802
 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802
 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802
        nan        nan        nan        nan        nan        nan
 0.74877532 0.74877532 0.74877532        nan        nan        nan
 0.75147802 0.75191855 0.75191855        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802
 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802 0.75147802
 0.75147802 0.75147802 0.75147802        nan        nan        nan
        nan        nan        nan 0.74877532 0.75191855 0.75191855
        nan        nan        nan 0.75147802 0.75191855 0.75191855
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75103749 0.75103749 0.75103749
 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749
 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749
        nan        nan        nan        nan        nan        nan
 0.74877532 0.74877532 0.74877532        nan        nan        nan
 0.75191855 0.74877532 0.74877532        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749
 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749
 0.75103749 0.75103749 0.75103749        nan        nan        nan
        nan        nan        nan 0.74877532 0.74877532 0.74877532
        nan        nan        nan 0.75191855 0.74877532 0.74877532
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75103749 0.75103749 0.75103749
 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749
 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749 0.75103749
        nan        nan        nan        nan        nan        nan
 0.74877532 0.74877532 0.74877532        nan        nan        nan
 0.75191855 0.74877532 0.74877532        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_esctasy

{'BernoulliNB': {'balanced_accuracy': 0.7277857152306492,
  'best_params_': {'alpha': 2, 'fit_prior': False},
  'test_balanced_accuracy': 0.6606442577030813},
 'LogisticRegression': {'balanced_accuracy': 0.7560618989033087,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'newton-cg',
   'tol': 0.001},
  'test_balanced_accuracy': 0.6386554621848739},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.5486344537815127},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6376750700280112},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6406162464985994},
 'SVC': {'balanced_accuracy': 0.7605029037848421,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.7044117647058823}}


drug_name = "Heroin"
results_heroin = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_heroin

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.7324498  0.7324498  0.7324498  0.7324498  0.7324498  0.7324498
 0.74170633 0.74170633 0.74170633 0.7324498  0.7324498  0.7324498
 0.7324498  0.7324498  0.7324498         nan        nan        nan
        nan        nan        nan 0.73484952 0.73484952 0.73484952
        nan        nan        nan 0.7262234  0.7262234  0.7262234
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7324498  0.7324498  0.7324498
 0.7324498  0.7324498  0.7324498  0.7350369  0.7350369  0.7350369
 0.7324498  0.7324498  0.7324498  0.7324498  0.7324498  0.7324498
        nan        nan        nan        nan        nan        nan
 0.73183139 0.73183139 0.73183139        nan        nan        nan
 0.7262234  0.7262234  0.7262234         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.7324498  0.7324498  0.7324498  0.7324498  0.7324498  0.7324498
 0.7324498  0.7324498  0.7324498  0.7324498  0.7324498  0.7324498
 0.7324498  0.7324498  0.7324498         nan        nan        nan
        nan        nan        nan 0.72536503 0.72536503 0.72536503
        nan        nan        nan 0.7262234  0.7262234  0.7262234
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74127715 0.74127715 0.74127715
 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715
 0.74084796 0.74127715 0.74127715 0.74213552 0.74127715 0.74127715
        nan        nan        nan        nan        nan        nan
 0.73484952 0.73484952 0.73484952        nan        nan        nan
 0.73484952 0.73484952 0.73484952        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715
 0.74127715 0.74127715 0.74127715 0.74084796 0.74127715 0.74127715
 0.74127715 0.74127715 0.74127715        nan        nan        nan
        nan        nan        nan 0.73484952 0.73484952 0.73484952
        nan        nan        nan 0.73484952 0.73484952 0.73484952
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74127715 0.74127715 0.74127715
 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715
 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715 0.74127715
        nan        nan        nan        nan        nan        nan
 0.73484952 0.73484952 0.73484952        nan        nan        nan
 0.73484952 0.73484952 0.73484952        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73309036 0.73309036 0.73309036 0.73266117 0.73266117 0.73266117
 0.7373942  0.7373942  0.7373942  0.73309036 0.73309036 0.73309036
 0.73266117 0.73309036 0.73309036        nan        nan        nan
        nan        nan        nan 0.73481909 0.73481909 0.73481909
        nan        nan        nan 0.73782338 0.73782338 0.7373942
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73309036 0.73309036 0.73309036
 0.73266117 0.73266117 0.73266117 0.73911278 0.73911278 0.73911278
 0.73309036 0.73309036 0.73309036 0.73266117 0.73309036 0.73309036
        nan        nan        nan        nan        nan        nan
 0.73653398 0.73653398 0.73653398        nan        nan        nan
 0.73782338 0.73782338 0.7373942         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73309036 0.73309036 0.73309036 0.73266117 0.73266117 0.73266117
 0.73266117 0.73266117 0.73266117 0.73309036 0.73266117 0.73309036
 0.73266117 0.73309036 0.73309036        nan        nan        nan
        nan        nan        nan 0.7373942  0.73782338 0.73782338
        nan        nan        nan 0.73782338 0.73782338 0.7373942
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7373942  0.7373942  0.7373942
 0.7373942  0.7373942  0.7373942  0.7373942  0.7373942  0.7373942
 0.7373942  0.7373942  0.7373942  0.7373942  0.7373942  0.7373942
        nan        nan        nan        nan        nan        nan
 0.73481909 0.73481909 0.73481909        nan        nan        nan
 0.73481909 0.73481909 0.73481909        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.7373942  0.7373942  0.7373942  0.7373942  0.7373942  0.7373942
 0.7373942  0.7373942  0.7373942  0.7373942  0.7373942  0.7373942
 0.7373942  0.7373942  0.7373942         nan        nan        nan
        nan        nan        nan 0.73481909 0.73481909 0.73481909
        nan        nan        nan 0.73481909 0.73481909 0.73481909
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.7373942  0.7373942  0.7373942
 0.7373942  0.7373942  0.7373942  0.7373942  0.7373942  0.7373942
 0.7373942  0.7373942  0.7373942  0.7373942  0.7373942  0.7373942
        nan        nan        nan        nan        nan        nan
 0.73481909 0.73481909 0.73481909        nan        nan        nan
 0.73481909 0.73481909 0.73481909        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057        nan        nan        nan
        nan        nan        nan 0.73352139 0.73352139 0.73352139
        nan        nan        nan 0.73309221 0.73352139 0.73352139
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
        nan        nan        nan        nan        nan        nan
 0.73309221 0.73309221 0.73309221        nan        nan        nan
 0.73309221 0.73352139 0.73352139        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057        nan        nan        nan
        nan        nan        nan 0.73352139 0.73352139 0.73352139
        nan        nan        nan 0.73309221 0.73352139 0.73352139
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
        nan        nan        nan        nan        nan        nan
 0.73352139 0.73352139 0.73352139        nan        nan        nan
 0.73352139 0.73352139 0.73352139        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057        nan        nan        nan
        nan        nan        nan 0.73352139 0.73352139 0.73352139
        nan        nan        nan 0.73352139 0.73352139 0.73352139
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057 0.73395057
        nan        nan        nan        nan        nan        nan
 0.73309221 0.73352139 0.73352139        nan        nan        nan
 0.73352139 0.73352139 0.73352139        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_heroin

{'BernoulliNB': {'balanced_accuracy': 0.7300943585385764,
  'best_params_': {'alpha': 1, 'fit_prior': False},
  'test_balanced_accuracy': 0.7171440388791231},
 'LogisticRegression': {'balanced_accuracy': 0.7421355153795108,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': False,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'saga',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7152483369524006},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.6728535484093338},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6860545272808741},
 'Orignal_SVC': {'test_balanced_accuracy': 0.7241581360079964},
 'SVC': {'balanced_accuracy': 0.7391192288045372,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'rbf',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.7123013821390412}}


drug_name = "Ketamine"
results_ketamine = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_ketamine

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.69224049 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049
 0.69443876 0.69443876 0.69443876 0.69224049 0.69224049 0.69224049
 0.69224049 0.69224049 0.69224049        nan        nan        nan
        nan        nan        nan 0.69642716 0.69642716 0.69642716
        nan        nan        nan 0.69389307 0.69389307 0.69389307
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69224049 0.69224049 0.69224049
 0.69224049 0.69224049 0.69224049 0.69522088 0.69522088 0.69522088
 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049
        nan        nan        nan        nan        nan        nan
 0.69460889 0.69460889 0.69460889        nan        nan        nan
 0.69389307 0.69389307 0.69389307        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049
 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049 0.69224049
 0.69224049 0.69224049 0.69224049        nan        nan        nan
        nan        nan        nan 0.69538437 0.69538437 0.69538437
        nan        nan        nan 0.69389307 0.69389307 0.69389307
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69443876 0.69443876 0.69443876
 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876
 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876
        nan        nan        nan        nan        nan        nan
 0.69642716 0.69642716 0.69642716        nan        nan        nan
 0.69642716 0.69642716 0.69642716        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876
 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876
 0.69443876 0.69443876 0.69443876        nan        nan        nan
        nan        nan        nan 0.69642716 0.69642716 0.69642716
        nan        nan        nan 0.69642716 0.69642716 0.69642716
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.69443876 0.69443876 0.69443876
 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876 0.69443876
 0.69443876 0.69443876 0.69443876 0.69396927 0.69443876 0.69443876
        nan        nan        nan        nan        nan        nan
 0.69642716 0.69642716 0.69642716        nan        nan        nan
 0.69642716 0.69642716 0.69642716        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.6849364  0.6849364  0.6849364  0.6849364  0.6849364  0.6849364
 0.68642992 0.68642992 0.68642992 0.6849364  0.6849364  0.6849364
 0.6849364  0.6849364  0.6849364         nan        nan        nan
        nan        nan        nan 0.68502147 0.68502147 0.68502147
        nan        nan        nan 0.68305847 0.68258899 0.68258899
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.6849364  0.6849364  0.6849364
 0.6849364  0.6849364  0.6849364  0.68493862 0.68493862 0.68493862
 0.6849364  0.6849364  0.6849364  0.6849364  0.6849364  0.6849364
        nan        nan        nan        nan        nan        nan
 0.68548874 0.68595822 0.68595822        nan        nan        nan
 0.68305847 0.68258899 0.68258899        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.6849364  0.6849364  0.6849364  0.6849364  0.6849364  0.6849364
 0.6849364  0.6849364  0.6849364  0.6849364  0.6849364  0.6849364
 0.6849364  0.6849364  0.6849364         nan        nan        nan
        nan        nan        nan 0.68258899 0.68258899 0.68258899
        nan        nan        nan 0.68258899 0.68258899 0.68258899
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68642992 0.68642992 0.68642992
 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992
 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992
        nan        nan        nan        nan        nan        nan
 0.68502147 0.68502147 0.68502147        nan        nan        nan
 0.68502147 0.68502147 0.68502147        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992
 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992
 0.68642992 0.68642992 0.68642992        nan        nan        nan
        nan        nan        nan 0.68502147 0.68502147 0.68502147
        nan        nan        nan 0.68502147 0.68502147 0.68502147
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68642992 0.68642992 0.68642992
 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992
 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992 0.68642992
        nan        nan        nan        nan        nan        nan
 0.68502147 0.68502147 0.68502147        nan        nan        nan
 0.68502147 0.68502147 0.68502147        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965
 0.68353238 0.68306068 0.68306068 0.68399965 0.68399965 0.68399965
 0.68399965 0.68399965 0.68399965        nan        nan        nan
        nan        nan        nan 0.68596044 0.68596044 0.68596044
        nan        nan        nan 0.68399965 0.68446913 0.68446913
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68399965 0.68399965 0.68399965
 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965
 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965
        nan        nan        nan        nan        nan        nan
 0.68642992 0.68642992 0.68642992        nan        nan        nan
 0.68399965 0.68446913 0.68446913        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965
 0.68446913 0.68399965 0.68399965 0.68399965 0.68399965 0.68399965
 0.68399965 0.68399965 0.68399965        nan        nan        nan
        nan        nan        nan 0.68446913 0.68446913 0.68446913
        nan        nan        nan 0.68399965 0.68446913 0.68446913
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68306068 0.68306068 0.68306068
 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068
 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068
        nan        nan        nan        nan        nan        nan
 0.68596044 0.68596044 0.68596044        nan        nan        nan
 0.68596044 0.68596044 0.68596044        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068
 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068
 0.68306068 0.68306068 0.68306068        nan        nan        nan
        nan        nan        nan 0.68596044 0.68596044 0.68596044
        nan        nan        nan 0.68642992 0.68596044 0.68596044
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.68306068 0.68306068 0.68306068
 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068
 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068 0.68306068
        nan        nan        nan        nan        nan        nan
 0.68596044 0.68596044 0.68596044        nan        nan        nan
 0.68642992 0.68596044 0.68596044        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_ketamine

{'BernoulliNB': {'balanced_accuracy': 0.6814379432346949,
  'best_params_': {'alpha': 1, 'fit_prior': False},
  'test_balanced_accuracy': 0.6522181249301597},
 'LogisticRegression': {'balanced_accuracy': 0.6964271570972264,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.6522181249301597},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.5897306961671696},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6523522181249302},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6637613141133087},
 'SVC': {'balanced_accuracy': 0.6954815463256788,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.001},
  'test_balanced_accuracy': 0.6595597273438373}}


drug_name = "Legalh"
results_legalh = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_legalh

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.72367111 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111
 0.73785627 0.73785627 0.73785627 0.72367111 0.72367111 0.72367111
 0.72367111 0.72367111 0.72367111        nan        nan        nan
        nan        nan        nan 0.73297634 0.73297634 0.73297634
        nan        nan        nan 0.7338478  0.7338478  0.7338478
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72367111 0.72367111 0.72367111
 0.72367111 0.72367111 0.72367111 0.7261775  0.7261775  0.7261775
 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111
        nan        nan        nan        nan        nan        nan
 0.73297634 0.73297634 0.73297634        nan        nan        nan
 0.7338478  0.7338478  0.7338478         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111
 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111 0.72367111
 0.72367111 0.72367111 0.72367111        nan        nan        nan
        nan        nan        nan 0.73428449 0.73428449 0.73428449
        nan        nan        nan 0.7338478  0.7338478  0.7338478
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73785627 0.73785627 0.73785627
 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627
 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627
        nan        nan        nan        nan        nan        nan
 0.73297634 0.73297634 0.73297634        nan        nan        nan
 0.73297634 0.73297634 0.73297634        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627
 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627
 0.73785627 0.73785627 0.73785627        nan        nan        nan
        nan        nan        nan 0.73297634 0.73297634 0.73297634
        nan        nan        nan 0.73297634 0.73297634 0.73297634
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73785627 0.73785627 0.73785627
 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627
 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627 0.73785627
        nan        nan        nan        nan        nan        nan
 0.73297634 0.73297634 0.73297634        nan        nan        nan
 0.73297634 0.73297634 0.73297634        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712
 0.72896565 0.72896565 0.72896565 0.72983712 0.72983712 0.72983712
 0.72983712 0.72983712 0.72983712        nan        nan        nan
        nan        nan        nan 0.73018787 0.73018787 0.73018787
        nan        nan        nan 0.72975308 0.73261023 0.73261023
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72983712 0.72983712 0.72983712
 0.72983712 0.72983712 0.72983712 0.72940234 0.72940234 0.72940234
 0.72689594 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712
        nan        nan        nan        nan        nan        nan
 0.73018787 0.73018787 0.73018787        nan        nan        nan
 0.72975308 0.73261023 0.73261023        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712
 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712 0.72983712
 0.72983712 0.72983712 0.72983712        nan        nan        nan
        nan        nan        nan 0.73018787 0.73018787 0.73018787
        nan        nan        nan 0.72975308 0.73261023 0.73261023
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72852897 0.72852897 0.72852897
 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897
 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897
        nan        nan        nan        nan        nan        nan
 0.73018787 0.73018787 0.73018787        nan        nan        nan
 0.73018787 0.73018787 0.73018787        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897
 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897
 0.72852897 0.72852897 0.72852897        nan        nan        nan
        nan        nan        nan 0.73018787 0.73018787 0.73018787
        nan        nan        nan 0.73018787 0.73018787 0.73018787
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72852897 0.72852897 0.72852897
 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897
 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897 0.72852897
        nan        nan        nan        nan        nan        nan
 0.73018787 0.73018787 0.73018787        nan        nan        nan
 0.73018787 0.73018787 0.73018787        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046        nan        nan        nan
        nan        nan        nan 0.72352568 0.72352568 0.72352568
        nan        nan        nan 0.72396046 0.72396046 0.72396046
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
        nan        nan        nan        nan        nan        nan
 0.72352568 0.72396046 0.72352568        nan        nan        nan
 0.72396046 0.72396046 0.72396046        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046        nan        nan        nan
        nan        nan        nan 0.72396046 0.72396046 0.72396046
        nan        nan        nan 0.72396046 0.72396046 0.72396046
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72352378 0.72396046 0.72396046 0.72352378 0.72396046 0.72396046
        nan        nan        nan        nan        nan        nan
 0.72352568 0.72352568 0.72352568        nan        nan        nan
 0.72352568 0.72352568 0.72352568        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72352378 0.72396046 0.72396046
 0.72352378 0.72396046 0.72396046        nan        nan        nan
        nan        nan        nan 0.72352568 0.72352568 0.72352568
        nan        nan        nan 0.72352568 0.72352568 0.72352568
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.72396046 0.72396046 0.72396046
 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046 0.72396046
 0.72352378 0.72396046 0.72396046 0.72352378 0.72396046 0.72396046
        nan        nan        nan        nan        nan        nan
 0.72352568 0.72352568 0.72352568        nan        nan        nan
 0.72352568 0.72352568 0.72352568        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_legalh

{'BernoulliNB': {'balanced_accuracy': 0.7329516268250227,
  'best_params_': {'alpha': 1, 'fit_prior': False},
  'test_balanced_accuracy': 0.7140326009922041},
 'LogisticRegression': {'balanced_accuracy': 0.7378562733238351,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7264056224899598},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.6539688164422395},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.7344377510040161},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6923281360737066},
 'SVC': {'balanced_accuracy': 0.7325789879270486,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.748789274746043}}


drug_name = "LSD"
results_LSD = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_LSD

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.78909554 0.78909554 0.78909554 0.78909554 0.78909554 0.78909554
 0.79159554 0.79159554 0.79159554 0.78909554 0.78909554 0.78909554
 0.78909554 0.78909554 0.78909554        nan        nan        nan
        nan        nan        nan 0.78934619 0.78934619 0.78934619
        nan        nan        nan 0.78859184 0.78735727 0.78735727
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78909554 0.78909554 0.78909554
 0.78909554 0.78909554 0.78909554 0.79159554 0.79159554 0.79159554
 0.78909554 0.78909554 0.78909554 0.79034554 0.78909554 0.78909554
        nan        nan        nan        nan        nan        nan
 0.78934619 0.78934619 0.78934619        nan        nan        nan
 0.78859184 0.78735727 0.78735727        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78909554 0.78909554 0.78909554 0.78909554 0.78909554 0.78909554
 0.78909554 0.78909554 0.78909554 0.78909554 0.78909554 0.78909554
 0.78909554 0.78909554 0.78909554        nan        nan        nan
        nan        nan        nan 0.78968177 0.78968177 0.78968177
        nan        nan        nan 0.78859184 0.78735727 0.78735727
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79159554 0.79159554 0.79159554
 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554
 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554
        nan        nan        nan        nan        nan        nan
 0.78934619 0.78934619 0.78934619        nan        nan        nan
 0.78989264 0.78934619 0.78934619        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554
 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554
 0.79159554 0.79159554 0.79159554        nan        nan        nan
        nan        nan        nan 0.78934619 0.78934619 0.78934619
        nan        nan        nan 0.78989264 0.78934619 0.78934619
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79159554 0.79159554 0.79159554
 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554
 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554 0.79159554
        nan        nan        nan        nan        nan        nan
 0.78934619 0.78934619 0.78934619        nan        nan        nan
 0.78865807 0.78934619 0.78934619        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931        nan        nan        nan
        nan        nan        nan 0.79376294 0.79376294 0.79376294
        nan        nan        nan 0.79251294 0.79251294 0.79251294
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
        nan        nan        nan        nan        nan        nan
 0.79376294 0.79376294 0.79376294        nan        nan        nan
 0.79251294 0.79251294 0.79251294        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931        nan        nan        nan
        nan        nan        nan 0.79376294 0.79251294 0.79251294
        nan        nan        nan 0.79251294 0.79251294 0.79251294
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
        nan        nan        nan        nan        nan        nan
 0.79376294 0.79376294 0.79376294        nan        nan        nan
 0.79376294 0.79376294 0.79376294        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931        nan        nan        nan
        nan        nan        nan 0.79376294 0.79376294 0.79376294
        nan        nan        nan 0.79376294 0.79376294 0.79376294
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931 0.79289931
        nan        nan        nan        nan        nan        nan
 0.79376294 0.79376294 0.79376294        nan        nan        nan
 0.79376294 0.79376294 0.79376294        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728        nan        nan        nan
        nan        nan        nan 0.78933728 0.78933728 0.78933728
        nan        nan        nan 0.78933728 0.78933728 0.78933728
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
        nan        nan        nan        nan        nan        nan
 0.78933728 0.78933728 0.78933728        nan        nan        nan
 0.78933728 0.78933728 0.78933728        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728        nan        nan        nan
        nan        nan        nan 0.78933728 0.78933728 0.78933728
        nan        nan        nan 0.78933728 0.78933728 0.78933728
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
        nan        nan        nan        nan        nan        nan
 0.78933728 0.78933728 0.78933728        nan        nan        nan
 0.78933728 0.78933728 0.78933728        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728        nan        nan        nan
        nan        nan        nan 0.78933728 0.78933728 0.78933728
        nan        nan        nan 0.78933728 0.78933728 0.78933728
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728 0.78933728
        nan        nan        nan        nan        nan        nan
 0.78933728 0.78933728 0.78933728        nan        nan        nan
 0.78933728 0.78933728 0.78933728        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_LSD

{'BernoulliNB': {'balanced_accuracy': 0.7775136392034705,
  'best_params_': {'alpha': 1, 'fit_prior': True},
  'test_balanced_accuracy': 0.7722229227083596},
 'LogisticRegression': {'balanced_accuracy': 0.7937629352445817,
  'best_params_': {'C': 1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7901273483797755},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.7722229227083596},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.7921605093935191},
 'Orignal_SVC': {'test_balanced_accuracy': 0.7873061404614803},
 'SVC': {'balanced_accuracy': 0.7970449015777479,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.7941936704072626}}


drug_name = "Meth"
results_meth = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_meth

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.75373968 0.75373968 0.75373968 0.75422749 0.75422749 0.75422749
 0.75374647 0.75374647 0.75374647 0.75373968 0.75373968 0.75373968
 0.75373968 0.75373968 0.75373968        nan        nan        nan
        nan        nan        nan 0.74698275 0.74698275 0.74698275
        nan        nan        nan 0.74150855 0.74150855 0.74150855
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75373968 0.75373968 0.75373968
 0.75422749 0.75422749 0.75422749 0.75400041 0.75400041 0.75400041
 0.75373968 0.75422749 0.75373968 0.75373968 0.75373968 0.75373968
        nan        nan        nan        nan        nan        nan
 0.74574642 0.74623422 0.74623422        nan        nan        nan
 0.74150855 0.74150855 0.74150855        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75373968 0.75373968 0.75373968 0.75422749 0.75422749 0.75422749
 0.75373968 0.75373968 0.75373968 0.75373968 0.75422749 0.75373968
 0.75422749 0.75373968 0.75373968        nan        nan        nan
        nan        nan        nan 0.74248416 0.74248416 0.74248416
        nan        nan        nan 0.74150855 0.74150855 0.74150855
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75374647 0.75374647 0.75374647
 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647
 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647
        nan        nan        nan        nan        nan        nan
 0.74698275 0.74698275 0.74698275        nan        nan        nan
 0.74698275 0.74698275 0.74698275        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647
 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647
 0.75374647 0.75374647 0.75374647        nan        nan        nan
        nan        nan        nan 0.74698275 0.74698275 0.74698275
        nan        nan        nan 0.74698275 0.74698275 0.74698275
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75374647 0.75374647 0.75374647
 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647
 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647 0.75374647
        nan        nan        nan        nan        nan        nan
 0.74698275 0.74698275 0.74698275        nan        nan        nan
 0.74698275 0.74698275 0.74698275        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896
 0.75396645 0.75396645 0.75396645 0.75079896 0.75079896 0.75079896
 0.75079896 0.75079896 0.75079896        nan        nan        nan
        nan        nan        nan 0.75129829 0.75081049 0.75081049
        nan        nan        nan 0.74712834 0.74712834 0.74712834
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75079896 0.75079896 0.75079896
 0.75079896 0.75079896 0.75079896 0.75249388 0.75249388 0.75249388
 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896
        nan        nan        nan        nan        nan        nan
 0.7517861  0.74882326 0.74882326        nan        nan        nan
 0.74712834 0.74712834 0.74712834        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896
 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896 0.75079896
 0.75079896 0.75079896 0.75079896        nan        nan        nan
        nan        nan        nan 0.74712834 0.74712834 0.74712834
        nan        nan        nan 0.74712834 0.74712834 0.74712834
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75396645 0.75396645 0.75396645
 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645
 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645
        nan        nan        nan        nan        nan        nan
 0.75129829 0.75081049 0.75081049        nan        nan        nan
 0.75081049 0.75081049 0.75081049        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645
 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645
 0.75396645 0.75396645 0.75396645        nan        nan        nan
        nan        nan        nan 0.75129829 0.75081049 0.75081049
        nan        nan        nan 0.75081049 0.75081049 0.75081049
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75396645 0.75396645 0.75396645
 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645
 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645 0.75396645
        nan        nan        nan        nan        nan        nan
 0.75081049 0.75081049 0.75081049        nan        nan        nan
 0.75081049 0.75081049 0.75081049        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096        nan        nan        nan
        nan        nan        nan 0.74884096 0.74884096 0.74884096
        nan        nan        nan 0.74711682 0.74711682 0.74711682
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
        nan        nan        nan        nan        nan        nan
 0.74884096 0.74711682 0.74711682        nan        nan        nan
 0.74711682 0.74711682 0.74711682        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096        nan        nan        nan
        nan        nan        nan 0.74711682 0.74711682 0.74711682
        nan        nan        nan 0.74711682 0.74711682 0.74711682
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
        nan        nan        nan        nan        nan        nan
 0.74884096 0.74884096 0.74884096        nan        nan        nan
 0.74884096 0.74884096 0.74884096        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096        nan        nan        nan
        nan        nan        nan 0.74884096 0.74884096 0.74884096
        nan        nan        nan 0.74884096 0.74884096 0.74884096
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096 0.74884096
        nan        nan        nan        nan        nan        nan
 0.74884096 0.74884096 0.74884096        nan        nan        nan
 0.74884096 0.74884096 0.74884096        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_meth

{'BernoulliNB': {'balanced_accuracy': 0.749612847625642,
  'best_params_': {'alpha': 7, 'fit_prior': False},
  'test_balanced_accuracy': 0.7603999416143629},
 'LogisticRegression': {'balanced_accuracy': 0.7542274881936372,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'lbfgs',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7631550138665888},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.7552911983652022},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.7608925704276748},
 'Orignal_SVC': {'test_balanced_accuracy': 0.7455480951685884},
 'SVC': {'balanced_accuracy': 0.7435117229089594,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'sigmoid',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.7450554663552766}}


drug_name = "Mushrooms"
results_mushrooms = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_mushrooms

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.77597037 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037
 0.77413362 0.77413362 0.77413362 0.77536062 0.77597037 0.77597037
 0.77536062 0.77597037 0.77597037        nan        nan        nan
        nan        nan        nan 0.77479216 0.77479216 0.77479216
        nan        nan        nan 0.77713362 0.77652387 0.77652387
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.77597037 0.77597037 0.77597037
 0.77597037 0.77597037 0.77597037 0.77474338 0.77474338 0.77474338
 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037
        nan        nan        nan        nan        nan        nan
 0.77479216 0.77479216 0.77479216        nan        nan        nan
 0.77652387 0.77652387 0.77652387        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037
 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037 0.77597037
 0.77597037 0.77597037 0.77597037        nan        nan        nan
        nan        nan        nan 0.77774338 0.77774338 0.77774338
        nan        nan        nan 0.77652387 0.77652387 0.77652387
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.77413362 0.77413362 0.77413362
 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362
 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362
        nan        nan        nan        nan        nan        nan
 0.77479216 0.77479216 0.77479216        nan        nan        nan
 0.77579216 0.77479216 0.77479216        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362
 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362
 0.77413362 0.77413362 0.77413362        nan        nan        nan
        nan        nan        nan 0.77479216 0.77479216 0.77479216
        nan        nan        nan 0.77479216 0.77479216 0.77479216
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.77413362 0.77413362 0.77413362
 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362
 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362 0.77413362
        nan        nan        nan        nan        nan        nan
 0.77479216 0.77479216 0.77479216        nan        nan        nan
 0.77479216 0.77479216 0.77479216        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517
 0.77334191 0.77273216 0.77273216 0.77456517 0.77456517 0.77456517
 0.77456517 0.77456517 0.77456517        nan        nan        nan
        nan        nan        nan 0.77012988 0.77012988 0.77012988
        nan        nan        nan 0.77152013 0.77152013 0.77152013
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.77456517 0.77456517 0.77456517
 0.77456517 0.77456517 0.77456517 0.77395167 0.77334191 0.77334191
 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517
        nan        nan        nan        nan        nan        nan
 0.77051638 0.77212988 0.77212988        nan        nan        nan
 0.77152013 0.77152013 0.77152013        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517
 0.77395541 0.77456517 0.77456517 0.77456517 0.77456517 0.77456517
 0.77395541 0.77456517 0.77456517        nan        nan        nan
        nan        nan        nan 0.77252013 0.77252013 0.77252013
        nan        nan        nan 0.77152013 0.77152013 0.77152013
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.77273216 0.77273216 0.77273216
 0.77273216 0.77273216 0.77273216 0.77334191 0.77273216 0.77273216
 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216
        nan        nan        nan        nan        nan        nan
 0.77012988 0.77012988 0.77012988        nan        nan        nan
 0.77012988 0.77012988 0.77012988        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216
 0.77334191 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216
 0.77273216 0.77273216 0.77273216        nan        nan        nan
        nan        nan        nan 0.76951638 0.77012988 0.77012988
        nan        nan        nan 0.77012988 0.77012988 0.77012988
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.77273216 0.77273216 0.77273216
 0.77273216 0.77273216 0.77273216 0.77334191 0.77273216 0.77273216
 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216 0.77273216
        nan        nan        nan        nan        nan        nan
 0.76951638 0.77012988 0.77012988        nan        nan        nan
 0.77012988 0.77012988 0.77012988        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
 0.77056142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142        nan        nan        nan
        nan        nan        nan 0.77056142 0.77056142 0.77056142
        nan        nan        nan 0.77056142 0.77056142 0.77056142
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.76956142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142 0.77056142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142 0.77056142 0.76956142 0.76956142
        nan        nan        nan        nan        nan        nan
 0.77056142 0.77056142 0.77056142        nan        nan        nan
 0.77056142 0.77056142 0.77056142        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
 0.77056142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
 0.77056142 0.76956142 0.76956142        nan        nan        nan
        nan        nan        nan 0.77156142 0.77056142 0.77056142
        nan        nan        nan 0.77056142 0.77056142 0.77056142
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.76956142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142 0.77056142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
        nan        nan        nan        nan        nan        nan
 0.77056142 0.77056142 0.77056142        nan        nan        nan
 0.77056142 0.77056142 0.77056142        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
 0.77056142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142        nan        nan        nan
        nan        nan        nan 0.77056142 0.77056142 0.77056142
        nan        nan        nan 0.77056142 0.77056142 0.77056142
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.76956142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142 0.77056142 0.76956142 0.76956142
 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142 0.76956142
        nan        nan        nan        nan        nan        nan
 0.77056142 0.77056142 0.77056142        nan        nan        nan
 0.77056142 0.77056142 0.77056142        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_mushrooms

{'BernoulliNB': {'balanced_accuracy': 0.7626908573993715,
  'best_params_': {'alpha': 6, 'fit_prior': False},
  'test_balanced_accuracy': 0.7604755570335883},
 'LogisticRegression': {'balanced_accuracy': 0.7777433787221308,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 10,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.7504988360492184},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.762484757787385},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.7440001108524554},
 'Orignal_SVC': {'test_balanced_accuracy': 0.7589097661013191},
 'SVC': {'balanced_accuracy': 0.7709573544815204,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'rbf',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.7484896352954218}}


drug_name = "Nicotine"
results_nicotine = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_nicotine

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.66191189 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189
 0.66246044 0.66246044 0.66246044 0.66191189 0.66191189 0.66191189
 0.66191189 0.66191189 0.66191189        nan        nan        nan
        nan        nan        nan 0.66508717 0.66508717 0.66508717
        nan        nan        nan 0.66413632 0.66413632 0.66413632
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.66191189 0.66191189 0.66191189
 0.66191189 0.66191189 0.66191189 0.66260633 0.66260633 0.66260633
 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189
        nan        nan        nan        nan        nan        nan
 0.66508717 0.66508717 0.66508717        nan        nan        nan
 0.66413632 0.66413632 0.66413632        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189
 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189 0.66191189
 0.66191189 0.66191189 0.66191189        nan        nan        nan
        nan        nan        nan 0.66552042 0.66705041 0.66705041
        nan        nan        nan 0.66413632 0.66413632 0.66413632
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.66246044 0.66246044 0.66246044
 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044
 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044
        nan        nan        nan        nan        nan        nan
 0.66508717 0.66508717 0.66508717        nan        nan        nan
 0.66508717 0.66508717 0.66508717        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044
 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044
 0.66246044 0.66246044 0.66246044        nan        nan        nan
        nan        nan        nan 0.66508717 0.66508717 0.66508717
        nan        nan        nan 0.66508717 0.66508717 0.66508717
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.66246044 0.66246044 0.66246044
 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044
 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044 0.66246044
        nan        nan        nan        nan        nan        nan
 0.66439752 0.66508717 0.66508717        nan        nan        nan
 0.66508717 0.66508717 0.66508717        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044
 0.6578701  0.6578701  0.6578701  0.65871044 0.65871044 0.65871044
 0.65871044 0.65871044 0.65871044        nan        nan        nan
        nan        nan        nan 0.66121744 0.6619071  0.6619071
        nan        nan        nan 0.66052779 0.66052779 0.66052779
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.65871044 0.65871044 0.65871044
 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044
 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044
        nan        nan        nan        nan        nan        nan
 0.6619071  0.66121744 0.66121744        nan        nan        nan
 0.66052779 0.66052779 0.66052779        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044
 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044 0.65871044
 0.65871044 0.65871044 0.65871044        nan        nan        nan
        nan        nan        nan 0.66052779 0.66052779 0.66052779
        nan        nan        nan 0.66052779 0.66052779 0.66052779
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.6578701  0.6578701  0.6578701
 0.6578701  0.6578701  0.6578701  0.6578701  0.6578701  0.6578701
 0.6578701  0.6578701  0.6578701  0.6578701  0.6578701  0.6578701
        nan        nan        nan        nan        nan        nan
 0.66121744 0.6619071  0.6619071         nan        nan        nan
 0.6619071  0.6619071  0.6619071         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.6578701  0.6578701  0.6578701  0.6578701  0.6578701  0.6578701
 0.6578701  0.6578701  0.6578701  0.6578701  0.6578701  0.6578701
 0.6578701  0.6578701  0.6578701         nan        nan        nan
        nan        nan        nan 0.6619071  0.6619071  0.6619071
        nan        nan        nan 0.6619071  0.6619071  0.6619071
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.6578701  0.6578701  0.6578701
 0.6578701  0.6578701  0.6578701  0.6578701  0.6578701  0.6578701
 0.6578701  0.6578701  0.6578701  0.6578701  0.6578701  0.6578701
        nan        nan        nan        nan        nan        nan
 0.66121744 0.6619071  0.6619071         nan        nan        nan
 0.6619071  0.6619071  0.6619071         nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65771942 0.65771942 0.65855275
 0.65771942 0.65771942 0.65855275        nan        nan        nan
        nan        nan        nan 0.65855275 0.65855275 0.65855275
        nan        nan        nan 0.65785831 0.65785831 0.65785831
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65771942 0.65855275 0.65855275
 0.65771942 0.65771942 0.65855275 0.65771942 0.65771942 0.65855275
        nan        nan        nan        nan        nan        nan
 0.65855275 0.65785831 0.65785831        nan        nan        nan
 0.65785831 0.65785831 0.65785831        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275
 0.65855275 0.65771942 0.65855275 0.65771942 0.65771942 0.65855275
 0.65771942 0.65771942 0.65855275        nan        nan        nan
        nan        nan        nan 0.65785831 0.65785831 0.65785831
        nan        nan        nan 0.65785831 0.65785831 0.65785831
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65771942 0.65855275 0.65855275
        nan        nan        nan        nan        nan        nan
 0.65785831 0.65855275 0.65785831        nan        nan        nan
 0.65785831 0.65785831 0.65855275        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275
 0.65771942 0.65855275 0.65855275        nan        nan        nan
        nan        nan        nan 0.65785831 0.65785831 0.65855275
        nan        nan        nan 0.65785831 0.65785831 0.65855275
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275 0.65855275
 0.65855275 0.65855275 0.65855275 0.65771942 0.65855275 0.65855275
        nan        nan        nan        nan        nan        nan
 0.65785831 0.65785831 0.65855275        nan        nan        nan
 0.65785831 0.65785831 0.65855275        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_nicotine

{'BernoulliNB': {'balanced_accuracy': 0.6579220193824655,
  'best_params_': {'alpha': 8, 'fit_prior': False},
  'test_balanced_accuracy': 0.674347158218126},
 'LogisticRegression': {'balanced_accuracy': 0.6670504121188705,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 10,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l1',
   'solver': 'liblinear',
   'tol': 0.0001},
  'test_balanced_accuracy': 0.6657362656575874},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.6793238669713886},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.6586177612928203},
 'Orignal_SVC': {'test_balanced_accuracy': 0.6735541318546826},
 'SVC': {'balanced_accuracy': 0.6735480456550436,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'scale',
   'kernel': 'rbf',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.6735541318546826}}


drug_name = "VSA"
results_VSA = hyperparam_tuning(y_train[drug_name],y_test[drug_name])
best_tuned_model[drug_name] = results_VSA

/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
2160 fits failed out of a total of 4050.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 459, in _check_solver
    solver
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1461, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 449, in _check_solver
    % (solver, penalty)
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
270 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 681, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py", line 1473, in fit
    % self.l1_ratio
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.74592998 0.74592998 0.74592998 0.74592998 0.74592998 0.74592998
 0.75294029 0.75294029 0.75294029 0.74636102 0.74592998 0.74592998
 0.74592998 0.74592998 0.74592998        nan        nan        nan
        nan        nan        nan 0.75208837 0.75208837 0.75208837
        nan        nan        nan 0.74776743 0.74776743 0.74776743
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74592998 0.74592998 0.74592998
 0.74592998 0.74592998 0.74592998 0.75196491 0.75196491 0.75196491
 0.74635917 0.74592998 0.74592998 0.74592998 0.74592998 0.74592998
        nan        nan        nan        nan        nan        nan
 0.7505793  0.7505793  0.7505793         nan        nan        nan
 0.74776743 0.74776743 0.74776743        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74592998 0.74592998 0.74592998 0.74592998 0.74592998 0.74592998
 0.74549895 0.74549895 0.74549895 0.74592998 0.74592998 0.74592998
 0.74549895 0.74592998 0.74592998        nan        nan        nan
        nan        nan        nan 0.75089243 0.75089243 0.75089243
        nan        nan        nan 0.74776743 0.74776743 0.74776743
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75294029 0.75294029 0.75294029
 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029
 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029
        nan        nan        nan        nan        nan        nan
 0.75208837 0.75208837 0.75208837        nan        nan        nan
 0.75165733 0.75208837 0.75208837        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029
 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029
 0.75294029 0.75294029 0.75294029        nan        nan        nan
        nan        nan        nan 0.75208837 0.75208837 0.75208837
        nan        nan        nan 0.75208837 0.75208837 0.75208837
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.75294029 0.75294029 0.75294029
 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029
 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029 0.75294029
        nan        nan        nan        nan        nan        nan
 0.75208837 0.75208837 0.75208837        nan        nan        nan
 0.75208837 0.75208837 0.75208837        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196
 0.74506651 0.74506651 0.74506651 0.74356299 0.74313196 0.74313196
 0.74313196 0.74313196 0.74313196        nan        nan        nan
        nan        nan        nan 0.74355929 0.74355929 0.74355929
        nan        nan        nan 0.74076082 0.74076082 0.74076082
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74313196 0.74313196 0.74313196
 0.74313196 0.74313196 0.74313196 0.74270092 0.74270092 0.74270092
 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196
        nan        nan        nan        nan        nan        nan
 0.74033349 0.74033349 0.74033349        nan        nan        nan
 0.74076082 0.74076082 0.74076082        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196
 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196 0.74313196
 0.74313196 0.74313196 0.74313196        nan        nan        nan
        nan        nan        nan 0.74033164 0.74076082 0.74076082
        nan        nan        nan 0.74076082 0.74076082 0.74076082
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74506651 0.74506651 0.74506651
 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651
 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651
        nan        nan        nan        nan        nan        nan
 0.74355929 0.74355929 0.74355929        nan        nan        nan
 0.74312826 0.74355929 0.74355929        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651
 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651
 0.74506651 0.74506651 0.74506651        nan        nan        nan
        nan        nan        nan 0.74312826 0.74355929 0.74355929
        nan        nan        nan 0.74312826 0.74355929 0.74355929
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74506651 0.74506651 0.74506651
 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651 0.74506651
 0.7418407  0.74506651 0.74506651 0.74506651 0.74506651 0.74506651
        nan        nan        nan        nan        nan        nan
 0.74312826 0.74355929 0.74355929        nan        nan        nan
 0.74312826 0.74355929 0.74355929        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654
 0.74259132 0.74259132 0.74259132 0.73979654 0.73979654 0.73979654
 0.73979654 0.73979654 0.73979654        nan        nan        nan
        nan        nan        nan 0.73893447 0.73893447 0.73893447
        nan        nan        nan 0.7361397  0.73893447 0.73893447
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.73979654 0.73979654 0.73979654
 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654
 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654
        nan        nan        nan        nan        nan        nan
 0.73570867 0.73570867 0.73570867        nan        nan        nan
 0.7361397  0.73893447 0.73893447        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654
 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654 0.73979654
 0.73979654 0.73979654 0.73979654        nan        nan        nan
        nan        nan        nan 0.73893447 0.73893447 0.73893447
        nan        nan        nan 0.7361397  0.73893447 0.73893447
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74259132 0.74259132 0.74259132
 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132
 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132
        nan        nan        nan        nan        nan        nan
 0.73893447 0.73893447 0.73893447        nan        nan        nan
 0.74216028 0.73893447 0.73893447        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132
 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132
 0.74259132 0.74259132 0.74259132        nan        nan        nan
        nan        nan        nan 0.73893447 0.73893447 0.73893447
        nan        nan        nan 0.74216028 0.73893447 0.73893447
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.74259132 0.74259132 0.74259132
 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132
 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132 0.74259132
        nan        nan        nan        nan        nan        nan
 0.73893447 0.73893447 0.73893447        nan        nan        nan
 0.74216028 0.73893447 0.73893447        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  category=UserWarning,


results_VSA

{'BernoulliNB': {'balanced_accuracy': 0.7597151788825925,
  'best_params_': {'alpha': 2, 'fit_prior': False},
  'test_balanced_accuracy': 0.7250826641473784},
 'LogisticRegression': {'balanced_accuracy': 0.7529402901172977,
  'best_params_': {'C': 0.1,
   'class_weight': 'balanced',
   'fit_intercept': True,
   'intercept_scaling': 0.1,
   'max_iter': 10000,
   'multi_class': 'ovr',
   'penalty': 'l2',
   'solver': 'liblinear',
   'tol': 0.001},
  'test_balanced_accuracy': 0.743852288199172},
 'Orignal_BernoulliNB': {'test_balanced_accuracy': 0.6979076940176165},
 'Orignal_LogisticRegression': {'test_balanced_accuracy': 0.7479090833310178},
 'Orignal_SVC': {'test_balanced_accuracy': 0.7217760982522436},
 'SVC': {'balanced_accuracy': 0.7711153337995961,
  'best_params_': {'class_weight': 'balanced',
   'gamma': 'auto',
   'kernel': 'rbf',
   'probability': True,
   'shrinking': True,
   'tol': 0.01},
  'test_balanced_accuracy': 0.7309177804329101}}


# SAVE
with open("best_params_score_models.pkl", "wb") as pkl_handle:
	pickle.dump(best_tuned_model, pkl_handle)


best_models_drugs = dict()
for name in best_tuned_model:
    list_acc = list()
    list_model = list()
    for model, element in best_tuned_model[name].items():
        list_acc.append(element["test_balanced_accuracy"])
        list_model.append(model)
    index_max = np.argmax(list_acc)
    if (index_max<len(models) or index_max==len(best_tuned_model[name])-1):
        best_model = models[index_max]
    else:
        best_model = models_without_params[index_max-len(models)](**best_tuned_model[name][list_model[index_max]]['best_params_'])
    best_models_drugs[name] = best_model


for name in best_models_drugs:
    best_models_drugs[name].fit(X_train, y_train[name])
    preds = best_models_drugs[name].predict(X_test)
    print(balanced_accuracy_score(y_test[name],preds))

0.6295633828655293
0.6870498352433441
0.6800907425907425
0.7430581970330924
0.6021472444830109
0.8144500867776563
0.6831339766107876
0.7112067264042947
0.7044117647058823
0.7241581360079964
0.6637613141133087
0.748789274746043
0.7941936704072626
0.7631550138665888
0.762484757787385
0.6793238669713886
0.7479090833310178


# We defined the selected models
models = [LogisticRegression(class_weight="balanced", max_iter=1000), SVC(class_weight="balanced", probability=True), BernoulliNB()]
models_without_params = [LogisticRegression, SVC]


# We load the best paramters for each models obtained by tuning
with open("best_params_score_models.pkl", "rb") as pkl_handle:
	  best_tuned_model = pickle.load(pkl_handle)


# We create a dictionnary that contains the best model for each drug
best_models_drugs = dict()
for name in best_tuned_model:
    list_acc = list()
    list_model = list()
    for model, element in best_tuned_model[name].items():
        list_acc.append(element["test_balanced_accuracy"])
        list_model.append(model)
    index_max = np.argmax(list_acc)
    if (index_max<len(models) or index_max==len(best_tuned_model[name])-1):
        best_model = models[index_max]
    else:
        best_model = models_without_params[index_max-len(models)](**best_tuned_model[name][list_model[index_max]]['best_params_'])
    best_models_drugs[name] = best_model


def evaluation_report(name):
    """
    Fit the best model for each drug and display 4 metrics:
    - Balanced accuracy score
    - Classification report
    - Confusion matric (Heatmap)
    - ROC curve
    It also display the importance of each variable (if available)

    Parameters
    ----------
    name: str
        Drug name
    """
    best_models_drugs[name].fit(X_train, y_train[name])
    preds = best_models_drugs[name].predict(X_test)

    # Balanced accuracy
    balanced_score = balanced_accuracy_score(y_test[name],preds)

    # Classification report
    classification_report_ = classification_report(y_test[name],preds)

    # Confusion matrix
    conf_mat = confusion_matrix(y_test[name], preds)
    conf_mat_normalized = 100*np.apply_along_axis(lambda x: x/x.sum(), axis=1, arr=conf_mat)

    # ROC curve, AUC
    probs = best_models_drugs[name].predict_proba(X_test)
    preds = probs[:,1]
    fpr, tpr, threshold = roc_curve(y_test[name], preds)
    roc_auc = auc(fpr, tpr)

    print(f"Evaluation {name}")
    print("Model used:",best_models_drugs[name])
    print("Balanced accuracy score:", balanced_score)
    print("AUC:", roc_auc)
    print("\nClassification report:", classification_report_,sep="\n")

    fig, axes = plt.subplots(1,2,figsize=(14,5))

    sns.heatmap(conf_mat_normalized, annot=True,vmin=0, vmax=100, fmt='.2f', cmap='Blues', ax=axes[0])
    axes[0].set_title('Confusion matrix for ' + name)
    axes[0].set_xlabel('Predicted label')
    axes[0].set_ylabel('True label')

    axes[1].set_title('ROC curve for ' + name)
    axes[1].plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
    axes[1].legend(loc = 'lower right')
    axes[1].plot([0, 1], [0, 1],'r--')
    axes[1].set_xlim([0, 1])
    axes[1].set_ylim([0, 1])
    axes[1].set_ylabel('True Positive Rate')
    axes[1].set_xlabel('False Positive Rate')
    plt.show()

    # Importance graph
    try:
        # get importance
        importance = best_models_drugs[name].coef_
        # plot feature importance
        plt.figure(figsize=(20,5))
        plt.bar([x for x in range(len(importance[0]))], importance[0])
        plt.xticks(np.arange(0,X_train.shape[1]),X_train.columns,rotation=45)
        plt.xlabel("Column")
        plt.ylabel("Importance")
        plt.title("Importance of each columns for this model")
        plt.show()
    except AttributeError:
        print("Importance not available")


evaluation_report("Alcohol")

Evaluation Alcohol
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.6295633828655293
AUC: 0.665978719501009

Classification report:
              precision    recall  f1-score   support

           0       0.25      0.63      0.36        92
           1       0.90      0.63      0.74       474

    accuracy                           0.63       566
   macro avg       0.57      0.63      0.55       566
weighted avg       0.79      0.63      0.68       566


evaluation_report("Amphet")

Evaluation Amphet
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.6870498352433441
AUC: 0.7754352200157466

Classification report:
              precision    recall  f1-score   support

           0       0.94      0.74      0.82       497
           1       0.25      0.64      0.36        69

    accuracy                           0.72       566
   macro avg       0.59      0.69      0.59       566
weighted avg       0.85      0.72      0.77       566


evaluation_report("Amyl")

Evaluation Amyl
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.6800907425907425
AUC: 0.7346403596403596

Classification report:
              precision    recall  f1-score   support

           0       0.90      0.68      0.77       462
           1       0.32      0.68      0.44       104

    accuracy                           0.68       566
   macro avg       0.61      0.68      0.61       566
weighted avg       0.80      0.68      0.71       566


evaluation_report("Benzos")

Evaluation Benzos
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.7430581970330924
AUC: 0.7977367820464055

Classification report:
              precision    recall  f1-score   support

           0       0.94      0.71      0.81       478
           1       0.33      0.77      0.46        88

    accuracy                           0.72       566
   macro avg       0.64      0.74      0.64       566
weighted avg       0.85      0.72      0.76       566


evaluation_report("Caff")

Evaluation Caff
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.6021472444830109
AUC: 0.6369931771391626

Classification report:
              precision    recall  f1-score   support

           0       0.33      0.59      0.42       137
           1       0.82      0.61      0.70       429

    accuracy                           0.61       566
   macro avg       0.58      0.60      0.56       566
weighted avg       0.70      0.61      0.64       566


evaluation_report("Cannabis")

Evaluation Cannabis
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.8144500867776563
AUC: 0.8839622035096741

Classification report:
              precision    recall  f1-score   support

           0       0.85      0.85      0.85       331
           1       0.78      0.78      0.78       235

    accuracy                           0.82       566
   macro avg       0.81      0.81      0.81       566
weighted avg       0.82      0.82      0.82       566


evaluation_report("Coke")

Evaluation Coke
Model used: SVC(class_weight='balanced', gamma='auto', probability=True, tol=0.01)
Balanced accuracy score: 0.6831339766107876
AUC: 0.7169727422295344

Classification report:
              precision    recall  f1-score   support

           0       0.96      0.72      0.82       523
           1       0.16      0.65      0.25        43

    accuracy                           0.71       566
   macro avg       0.56      0.68      0.54       566
weighted avg       0.90      0.71      0.78       566

Importance not available


evaluation_report("Crack")

Evaluation Crack
Model used: LogisticRegression(C=0.1, class_weight='balanced', intercept_scaling=0.1,
                   max_iter=10000, multi_class='ovr', penalty='l1',
                   solver='liblinear', tol=0.001)
Balanced accuracy score: 0.7112067264042947
AUC: 0.7639047882208976

Classification report:
              precision    recall  f1-score   support

           0       0.97      0.67      0.79       517
           1       0.18      0.76      0.29        49

    accuracy                           0.67       566
   macro avg       0.57      0.71      0.54       566
weighted avg       0.90      0.67      0.75       566


evaluation_report("Esctasy")

Evaluation Esctasy
Model used: SVC(class_weight='balanced', gamma='auto', kernel='sigmoid', probability=True,
    tol=0.01)
Balanced accuracy score: 0.7044117647058823
AUC: 0.7233193277310924

Classification report:
              precision    recall  f1-score   support

           0       0.96      0.66      0.78       510
           1       0.19      0.75      0.31        56

    accuracy                           0.67       566
   macro avg       0.58      0.70      0.55       566
weighted avg       0.88      0.67      0.73       566

Importance not available


evaluation_report("Heroin")

Evaluation Heroin
Model used: SVC(class_weight='balanced', probability=True)
Balanced accuracy score: 0.7241581360079964
AUC: 0.799365801537242

Classification report:
              precision    recall  f1-score   support

           0       0.96      0.75      0.84       509
           1       0.24      0.70      0.35        57

    accuracy                           0.74       566
   macro avg       0.60      0.72      0.60       566
weighted avg       0.88      0.74      0.79       566

Importance not available


evaluation_report("Ketamine")

Evaluation Ketamine
Model used: SVC(class_weight='balanced', probability=True)
Balanced accuracy score: 0.6637613141133087
AUC: 0.7221924237344953

Classification report:
              precision    recall  f1-score   support

           0       0.91      0.64      0.75       471
           1       0.28      0.68      0.40        95

    accuracy                           0.65       566
   macro avg       0.59      0.66      0.58       566
weighted avg       0.80      0.65      0.69       566

Importance not available


evaluation_report("Legalh")

Evaluation Legalh
Model used: SVC(class_weight='balanced', gamma='auto', kernel='sigmoid', probability=True,
    tol=0.01)
Balanced accuracy score: 0.748789274746043
AUC: 0.8062839593668792

Classification report:
              precision    recall  f1-score   support

           0       0.96      0.69      0.80       498
           1       0.26      0.81      0.40        68

    accuracy                           0.70       566
   macro avg       0.61      0.75      0.60       566
weighted avg       0.88      0.70      0.75       566

Importance not available


evaluation_report("LSD")

Evaluation LSD
Model used: SVC(class_weight='balanced', gamma='auto', kernel='sigmoid', probability=True,
    tol=0.01)
Balanced accuracy score: 0.7941936704072626
AUC: 0.863809733955365

Classification report:
              precision    recall  f1-score   support

           0       0.92      0.78      0.84       412
           1       0.58      0.81      0.67       154

    accuracy                           0.79       566
   macro avg       0.75      0.79      0.76       566
weighted avg       0.82      0.79      0.80       566

Importance not available


evaluation_report("Meth")

Evaluation Meth
Model used: LogisticRegression(C=0.1, class_weight='balanced', intercept_scaling=0.1,
                   max_iter=10000, multi_class='ovr', tol=0.001)
Balanced accuracy score: 0.7631550138665888
AUC: 0.8270690410159101

Classification report:
              precision    recall  f1-score   support

           0       0.92      0.76      0.83       442
           1       0.47      0.77      0.58       124

    accuracy                           0.76       566
   macro avg       0.70      0.76      0.71       566
weighted avg       0.82      0.76      0.78       566


evaluation_report("Mushrooms")

Evaluation Mushrooms
Model used: BernoulliNB()
Balanced accuracy score: 0.762484757787385
AUC: 0.835363318922514

Classification report:
              precision    recall  f1-score   support

           0       0.85      0.79      0.82       372
           1       0.65      0.73      0.69       194

    accuracy                           0.77       566
   macro avg       0.75      0.76      0.75       566
weighted avg       0.78      0.77      0.78       566

/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:103: FutureWarning: Attribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).
  warnings.warn(msg, category=FutureWarning)


evaluation_report("Nicotine")

Evaluation Nicotine
Model used: BernoulliNB()
Balanced accuracy score: 0.6793238669713886
AUC: 0.7412673435490116

Classification report:
              precision    recall  f1-score   support

           0       0.67      0.74      0.70       287
           1       0.70      0.62      0.66       279

    accuracy                           0.68       566
   macro avg       0.68      0.68      0.68       566
weighted avg       0.68      0.68      0.68       566

/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:103: FutureWarning: Attribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).
  warnings.warn(msg, category=FutureWarning)


evaluation_report("VSA")

Evaluation VSA
Model used: LogisticRegression(class_weight='balanced', max_iter=1000)
Balanced accuracy score: 0.7479090833310178
AUC: 0.8206118536219401

Classification report:
              precision    recall  f1-score   support

           0       0.95      0.74      0.83       493
           1       0.30      0.75      0.43        73

    accuracy                           0.74       566
   macro avg       0.63      0.75      0.63       566
weighted avg       0.87      0.74      0.78       566


model_names = list(best_models_drugs.keys())
model_scores = list()
for name in best_models_drugs:
    best_models_drugs[name].fit(X_train, y_train[name])
    preds = best_models_drugs[name].predict(X_test)
    model_scores.append(balanced_accuracy_score(y_test[name],preds))

final_scores = pd.DataFrame({"Balanced scores": model_scores}, index=model_names)
plt.figure(figsize=(3,7))
sns.heatmap(final_scores,annot=True,cmap = 'RdYlGn',vmax=1.0,vmin=0.0)
plt.title("Balanced accuracy score for each drug")
plt.show()


model_names = list(best_models_drugs.keys())
best_models_pickle = dict()
for name in best_models_drugs:
    m = best_models_drugs[name]
    m.fit(X_train, y_train[name])
    best_models_pickle[name] = m

pickle.dump(best_models_pickle, open( 'models' + ".p", "wb" ) )

	ID	Age	Gender	Education	Country	Ethnicity	Nscore	Escore	Oscore	Ascore	Cscore	Impulsive	SS
count	1885.000000	1885.00000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000	1885.000000
mean	945.294960	0.03461	-0.000256	-0.003806	0.355542	-0.309577	0.000047	-0.000163	-0.000534	-0.000245	-0.000386	0.007216	-0.003292
std	545.167641	0.87836	0.482588	0.950078	0.700335	0.166226	0.998106	0.997448	0.996229	0.997440	0.997523	0.954435	0.963701
min	1.000000	-0.95197	-0.482460	-2.435910	-0.570090	-1.107020	-3.464360	-3.273930	-3.273930	-3.464360	-3.464360	-2.555240	-2.078480
25%	474.000000	-0.95197	-0.482460	-0.611130	-0.570090	-0.316850	-0.678250	-0.695090	-0.717270	-0.606330	-0.652530	-0.711260	-0.525930
50%	946.000000	-0.07854	-0.482460	-0.059210	0.960820	-0.316850	0.042570	0.003320	-0.019280	-0.017290	-0.006650	-0.217120	0.079870
75%	1417.000000	0.49788	0.482460	0.454680	0.960820	-0.316850	0.629670	0.637790	0.723300	0.760960	0.584890	0.529750	0.765400
max	1888.000000	2.59171	0.482460	1.984370	0.960820	1.907250	3.273930	3.273930	2.901610	3.464360	3.464360	2.901610	1.921730

	ID	Age	Gender	Education	Country	Ethnicity	Nscore	Escore	Oscore	Ascore	Cscore	Impulsive	SS	Alcohol	Amphet	Amyl	Benzos	Caff	Cannabis	Choc	Coke	Crack	Esctasy	Heroin	Ketamine	Legalh	LSD	Meth	Mushrooms	Nicotine	Semer	VSA
0	1	0.49788	0.48246	-0.05921	0.96082	0.12600	0.31287	-0.57545	-0.58331	-0.91699	-0.00665	-0.21712	-1.18084	CL5	CL2	CL0	CL2	CL6	CL0	CL5	CL0	CL0	CL0	CL0	CL0	CL0	CL0	CL0	CL0	CL2	CL0	CL0
1	2	-0.07854	-0.48246	1.98437	0.96082	-0.31685	-0.67825	1.93886	1.43533	0.76096	-0.14277	-0.71126	-0.21575	CL5	CL2	CL2	CL0	CL6	CL4	CL6	CL3	CL0	CL4	CL0	CL2	CL0	CL2	CL3	CL0	CL4	CL0	CL0
2	3	0.49788	-0.48246	-0.05921	0.96082	-0.31685	-0.46725	0.80523	-0.84732	-1.62090	-1.01450	-1.37983	0.40148	CL6	CL0	CL0	CL0	CL6	CL3	CL4	CL0	CL0	CL0	CL0	CL0	CL0	CL0	CL0	CL1	CL0	CL0	CL0
3	4	-0.95197	0.48246	1.16365	0.96082	-0.31685	-0.14882	-0.80615	-0.01928	0.59042	0.58489	-1.37983	-1.18084	CL4	CL0	CL0	CL3	CL5	CL2	CL4	CL2	CL0	CL0	CL0	CL2	CL0	CL0	CL0	CL0	CL2	CL0	CL0
4	5	0.49788	0.48246	1.98437	0.96082	-0.31685	0.73545	-1.63340	-0.45174	-0.30172	1.30612	-0.21712	-0.21575	CL4	CL1	CL1	CL0	CL6	CL3	CL6	CL0	CL0	CL1	CL0	CL0	CL1	CL0	CL0	CL2	CL2	CL0	CL0

	Alcohol	Amphet	Amyl	Benzos	Caff	Cannabis	Coke	Crack	Esctasy	Heroin	Ketamine	Legalh	LSD	Meth	Mushrooms	Nicotine	VSA
Basic	0.537057	0.641137	0.630848	0.696819	0.557714	0.816025	0.527858	0.654857	0.560399	0.672854	0.589731	0.653969	0.772223	0.755291	0.762485	0.679324	0.697908
Weight	0.629563	0.691905	0.680091	0.743058	0.602147	0.81445	0.680911	0.693601	0.640616	0.724158	0.663761	0.734438	0.792161	0.760893	0.762485	0.679324	0.747909
Sampling	0.548088	0.641137	0.6163	0.696819	0.543634	0.797769	0.527858	0.654857	0.557458	0.672854	0.589731	0.653969	0.772223	0.755291	0.762485	0.689977	0.697908
Basic_model_name	SGDClassifier	BernoulliNB	DecisionTreeClassifier	BernoulliNB	SGDClassifier	SGDClassifier	BernoulliNB	BernoulliNB	DecisionTreeClassifier	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB
Weight_model_name	LogisticRegression	SGDClassifier	LogisticRegression	LogisticRegression	LogisticRegression	LogisticRegression	SVC	SVC	SVC	SVC	SVC	LogisticRegression	LogisticRegression	LogisticRegression	BernoulliNB	BernoulliNB	LogisticRegression
Sampling_model_name	BaggingClassifier	BernoulliNB	DecisionTreeClassifier	BernoulliNB	DecisionTreeClassifier	BernoulliNB	BernoulliNB	BernoulliNB	DecisionTreeClassifier	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	BernoulliNB	RandomForestClassifier	BernoulliNB
Best method	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Basic_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Weight_model_name	Basic_model_name	Sampling_model_name	Weight_model_name
Best model	LogisticRegression	SGDClassifier	LogisticRegression	LogisticRegression	LogisticRegression	SGDClassifier	SVC	SVC	SVC	SVC	SVC	LogisticRegression	LogisticRegression	LogisticRegression	BernoulliNB	RandomForestClassifier	LogisticRegression

Project - Python For Data Analysis 🐍¶

Quantified drug consumption 💊¶

How can we model the risk of addiction to a drug based on personnality test?

Table of Contents¶

ID column¶

Features¶

Age¶

Gender¶

Education¶

Country¶

Ethnicity¶

NEO-FFI-R score¶

BIS11¶

ImpSS¶

Target¶

Between Features¶

Features/Targets¶

Age - Drug used¶

Gender - Drug used¶

Education - Drug used¶

Country - Drug used¶

Ethnicity - Drug used¶

Between targets¶

Basic implementation¶

First metric: Accuracy¶

Second metric: Accuracy balanced (classes took into account)¶

Third Metric: Confusion matrix¶

Weighting (balanced classes)¶

Over sampling¶

Basic implementation¶

Weighting (balanced classes)¶

Over sampling¶

Comparison¶

Comparison with each method with confusion matrix¶

Confusion matrix for all drugs¶

Alcohol¶

Amphet¶

Amyl¶

Benzos¶

Caff¶

Cannabis¶

Coke¶

Crack¶

Esctasy¶

Heroin¶

Ketamine¶

Legalh¶

LSD¶

Meth¶

Mushrooms¶

Nicotine¶

VSA¶

Save data¶

Comparison¶

Save models¶